{"id":64,"date":"2026-08-17T16:22:48","date_gmt":"2026-08-17T07:22:48","guid":{"rendered":"https:\/\/www.db.sanken.osaka-u.ac.jp\/?page_id=64"},"modified":"2026-08-17T16:59:44","modified_gmt":"2026-08-17T07:59:44","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/publications\/","title":{"rendered":"PUBLICATIONS"},"content":{"rendered":"<div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><div class=\"teachpress_publication_list\"><h3 class=\"tp_h3\" id=\"tp_h3_2026\">2026<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Koki Kawabata; Pedro Fidalgo; Mirela Cazzolato; Saranya Vijayakumar; Yasuko Matsubara; Yasushi Sakurai; Christos Faloutsos<\/p><p class=\"tp_pub_title\">RoleMine: Mining Behavioral Patterns in Heterogeneous Call Graphs <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026), <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Rome, Italy, <\/span><span class=\"tp_pub_additional_year\">2026<\/span><span class=\"tp_pub_additional_note\">, (Accepted at CIKM 2026; DOI not yet independently verified as of 2026-08-17)<\/span>.<\/p><p class=\"tp_pub_menu\"><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_127\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B62b_2026_RoleMineMiningBehavioralPatterns,<br \/>\r\ntitle = {RoleMine: Mining Behavioral Patterns in Heterogeneous Call Graphs},<br \/>\r\nauthor = {Koki Kawabata and Pedro Fidalgo and Mirela Cazzolato and Saranya Vijayakumar and Yasuko Matsubara and Yasushi Sakurai and Christos Faloutsos},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026)},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Rome, Italy},<br \/>\r\nnote = {Accepted at CIKM 2026; DOI not yet independently verified as of 2026-08-17},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('127','tp_bibtex')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Rikuto Kotoge; Ziwei Yang; Zheng Chen; Yushun Dong; Yasuko Matsubara; Jimeng Sun; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('117','tp_links')\" style=\"cursor:pointer;\">Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the AAAI Conference on Artificial Intelligence, <\/span><span class=\"tp_pub_additional_pages\">pp. 534\u2013542, <\/span><span class=\"tp_pub_additional_publisher\">AAAI Press, <\/span><span class=\"tp_pub_additional_address\">Singapore, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_117\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('117','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_117\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B53_2026_TargetedPathwayInferencefor,<br \/>\r\ntitle = {Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation},<br \/>\r\nauthor = {Rikuto Kotoge and Ziwei Yang and Zheng Chen and Yushun Dong and Yasuko Matsubara and Jimeng Sun and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1609\/aaai.v40i1.37017},<br \/>\r\ndoi = {10.1609\/aaai.v40i1.37017},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},<br \/>\r\nvolume = {40},<br \/>\r\nnumber = {1},<br \/>\r\npages = {534\u2013542},<br \/>\r\npublisher = {AAAI Press},<br \/>\r\naddress = {Singapore},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('117','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_117\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1609\/aaai.v40i1.37017\" title=\"https:\/\/doi.org\/10.1609\/aaai.v40i1.37017\" target=\"_blank\">https:\/\/doi.org\/10.1609\/aaai.v40i1.37017<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1609\/aaai.v40i1.37017\" title=\"Follow DOI:10.1609\/aaai.v40i1.37017\" target=\"_blank\">doi:10.1609\/aaai.v40i1.37017<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('117','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kota Nakamura; Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('118','tp_links')\" style=\"cursor:pointer;\">Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), Vol. 1, <\/span><span class=\"tp_pub_additional_pages\">pp. 1089\u20131100, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Jeju, Korea, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_118\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('118','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_118\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B54_2026_FastMiningandDynamic,<br \/>\r\ntitle = {Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams},<br \/>\r\nauthor = {Kota Nakamura and Koki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3770854.3780164},<br \/>\r\ndoi = {10.1145\/3770854.3780164},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), Vol. 1},<br \/>\r\npages = {1089\u20131100},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Jeju, Korea},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('118','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_118\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3770854.3780164\" title=\"https:\/\/doi.org\/10.1145\/3770854.3780164\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3770854.3780164<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3770854.3780164\" title=\"Follow DOI:10.1145\/3770854.3780164\" target=\"_blank\">doi:10.1145\/3770854.3780164<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('118','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Shingo Higashiguchi; Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('119','tp_links')\" style=\"cursor:pointer;\">Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the ACM Web Conference 2026 (WWW 2026), <\/span><span class=\"tp_pub_additional_pages\">pp. 7485\u20137494, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Dubai, UAE, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_119\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('119','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_119\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B55_2026_InterpretableDynamicNetworkModeling,<br \/>\r\ntitle = {Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso},<br \/>\r\nauthor = {Shingo Higashiguchi and Koki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3774904.3792608},<br \/>\r\ndoi = {10.1145\/3774904.3792608},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the ACM Web Conference 2026 (WWW 2026)},<br \/>\r\npages = {7485\u20137494},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Dubai, UAE},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('119','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_119\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3774904.3792608\" title=\"https:\/\/doi.org\/10.1145\/3774904.3792608\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3774904.3792608<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3774904.3792608\" title=\"Follow DOI:10.1145\/3774904.3792608\" target=\"_blank\">doi:10.1145\/3774904.3792608<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('119','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Soshi Kakio; Yasuko Matsubara; Ren Fujiwara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('120','tp_links')\" style=\"cursor:pointer;\">Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the ACM Web Conference 2026 (WWW 2026), <\/span><span class=\"tp_pub_additional_pages\">pp. 6989\u20137000, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Dubai, UAE, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_120\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('120','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_120\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B56_2026_MultiAspectMiningand,<br \/>\r\ntitle = {Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams},<br \/>\r\nauthor = {Soshi Kakio and Yasuko Matsubara and Ren Fujiwara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3774904.3792175},<br \/>\r\ndoi = {10.1145\/3774904.3792175},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the ACM Web Conference 2026 (WWW 2026)},<br \/>\r\npages = {6989\u20137000},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Dubai, UAE},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('120','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_120\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3774904.3792175\" title=\"https:\/\/doi.org\/10.1145\/3774904.3792175\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3774904.3792175<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3774904.3792175\" title=\"Follow DOI:10.1145\/3774904.3792175\" target=\"_blank\">doi:10.1145\/3774904.3792175<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('120','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ren Fujiwara; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('121','tp_links')\" style=\"cursor:pointer;\">When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">The Fourteenth International Conference on Learning Representations (ICLR 2026), <\/span><span class=\"tp_pub_additional_address\">Rio de Janeiro, Brazil, <\/span><span class=\"tp_pub_additional_year\">2026<\/span><span class=\"tp_pub_additional_note\">, (ICLR 2026 Poster)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_121\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('121','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_121\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B57_2026_WhentoRetrainafter,<br \/>\r\ntitle = {When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency},<br \/>\r\nauthor = {Ren Fujiwara and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/openreview.net\/forum?id=05PqjBzN6S},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {The Fourteenth International Conference on Learning Representations (ICLR 2026)},<br \/>\r\naddress = {Rio de Janeiro, Brazil},<br \/>\r\nnote = {ICLR 2026 Poster},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('121','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_121\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/openreview.net\/forum?id=05PqjBzN6S\" title=\"https:\/\/openreview.net\/forum?id=05PqjBzN6S\" target=\"_blank\">https:\/\/openreview.net\/forum?id=05PqjBzN6S<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('121','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Haohui Jia; Zheng Chen; Lingwei Zhu; Rikuto Kotoge; Jathurshan Pradeepkumar; Yasuko Matsubara; Jimeng Sun; Yasushi Sakurai; Takashi Matsubara<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('122','tp_links')\" style=\"cursor:pointer;\">ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">The Fourteenth International Conference on Learning Representations (ICLR 2026), <\/span><span class=\"tp_pub_additional_address\">Rio de Janeiro, Brazil, <\/span><span class=\"tp_pub_additional_year\">2026<\/span><span class=\"tp_pub_additional_note\">, (ICLR 2026 Poster)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_122\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('122','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_122\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B58_2026_ODEBrainContinuousTimeEEG,<br \/>\r\ntitle = {ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks},<br \/>\r\nauthor = {Haohui Jia and Zheng Chen and Lingwei Zhu and Rikuto Kotoge and Jathurshan Pradeepkumar and Yasuko Matsubara and Jimeng Sun and Yasushi Sakurai and Takashi Matsubara},<br \/>\r\nurl = {https:\/\/openreview.net\/forum?id=9EjZGs8ube},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {The Fourteenth International Conference on Learning Representations (ICLR 2026)},<br \/>\r\naddress = {Rio de Janeiro, Brazil},<br \/>\r\nnote = {ICLR 2026 Poster},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('122','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_122\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/openreview.net\/forum?id=9EjZGs8ube\" title=\"https:\/\/openreview.net\/forum?id=9EjZGs8ube\" target=\"_blank\">https:\/\/openreview.net\/forum?id=9EjZGs8ube<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('122','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kohei Obata; Zheng Chen; Yasuko Matsubara; Lingwei Zhu; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('123','tp_links')\" style=\"cursor:pointer;\">AnomalyFilter: Selective Denoising Diffusion Model for Time Series Anomaly Detection<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2026), <\/span><span class=\"tp_pub_additional_pages\">pp. 429\u2013441, <\/span><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_address\">Hong Kong, China, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_123\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('123','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_123\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B59_2026_AnomalyFilterSelectiveDenoisingDiffu,<br \/>\r\ntitle = {AnomalyFilter: Selective Denoising Diffusion Model for Time Series Anomaly Detection},<br \/>\r\nauthor = {Kohei Obata and Zheng Chen and Yasuko Matsubara and Lingwei Zhu and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-981-92-1300-9_34},<br \/>\r\ndoi = {10.1007\/978-981-92-1300-9_34},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2026)},<br \/>\r\nvolume = {16597},<br \/>\r\npages = {429\u2013441},<br \/>\r\npublisher = {Springer},<br \/>\r\naddress = {Hong Kong, China},<br \/>\r\nseries = {Lecture Notes in Computer Science \/ Lecture Notes in Artificial Intelligence},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('123','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_123\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-981-92-1300-9_34\" title=\"https:\/\/doi.org\/10.1007\/978-981-92-1300-9_34\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-981-92-1300-9_34<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-981-92-1300-9_34\" title=\"Follow DOI:10.1007\/978-981-92-1300-9_34\" target=\"_blank\">doi:10.1007\/978-981-92-1300-9_34<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('123','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Naoki Chihara; Tatsushi Oka; Yasuko Matsubara; Yasushi Sakurai; Shota Yasui<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('124','tp_links')\" style=\"cursor:pointer;\">Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), <\/span><span class=\"tp_pub_additional_address\">Seoul, South Korea, <\/span><span class=\"tp_pub_additional_year\">2026<\/span><span class=\"tp_pub_additional_note\">, (Accepted at ICML 2026)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_124\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('124','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_124\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B60_2026_ModelingCovariateTransitionfor,<br \/>\r\ntitle = {Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments},<br \/>\r\nauthor = {Naoki Chihara and Tatsushi Oka and Yasuko Matsubara and Yasushi Sakurai and Shota Yasui},<br \/>\r\nurl = {https:\/\/arxiv.org\/abs\/2605.31443},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML 2026)},<br \/>\r\naddress = {Seoul, South Korea},<br \/>\r\nnote = {Accepted at ICML 2026},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('124','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_124\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-arxiv\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/arxiv.org\/abs\/2605.31443\" title=\"https:\/\/arxiv.org\/abs\/2605.31443\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2605.31443<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('124','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ren Fujiwara; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('125','tp_links')\" style=\"cursor:pointer;\">Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2026), <\/span><span class=\"tp_pub_additional_address\">Bremen, Germany, <\/span><span class=\"tp_pub_additional_year\">2026<\/span><span class=\"tp_pub_additional_note\">, (Accepted at IJCAI 2026)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_125\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('125','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_125\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B61_2026_ModelingDynamicMixturesof,<br \/>\r\ntitle = {Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series},<br \/>\r\nauthor = {Ren Fujiwara and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/arxiv.org\/abs\/2605.26191},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2026)},<br \/>\r\naddress = {Bremen, Germany},<br \/>\r\nnote = {Accepted at IJCAI 2026},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('125','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_125\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-arxiv\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/arxiv.org\/abs\/2605.26191\" title=\"https:\/\/arxiv.org\/abs\/2605.26191\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2605.26191<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('125','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Naoki Chihara; Ren Fujiwara; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('126','tp_links')\" style=\"cursor:pointer;\">AdaKoop: Efficient Modeling of Nonlinear Dynamics from Nonstationary Data Streams with Koopman Operator Regression<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), Vol. 2, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Jeju, Korea, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_126\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('126','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_126\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B62_2026_AdaKoopEfficientModelingof,<br \/>\r\ntitle = {AdaKoop: Efficient Modeling of Nonlinear Dynamics from Nonstationary Data Streams with Koopman Operator Regression},<br \/>\r\nauthor = {Naoki Chihara and Ren Fujiwara and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3770855.3817851},<br \/>\r\ndoi = {10.1145\/3770855.3817851},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nbooktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), Vol. 2},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Jeju, Korea},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('126','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_126\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3770855.3817851\" title=\"https:\/\/doi.org\/10.1145\/3770855.3817851\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3770855.3817851<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3770855.3817851\" title=\"Follow DOI:10.1145\/3770855.3817851\" target=\"_blank\">doi:10.1145\/3770855.3817851<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('126','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2025\">2025<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Motomi Mizugai; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\">Online Multi-Scale Anomaly Detection via HNSW-Driven Graph Clustering and Dynamic Centroid Updates <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 1\u201314, <\/span><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_address\">Kyoto, Japan, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_112\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B48_2025_OnlineMultiScaleAnomaly,<br \/>\r\ntitle = {Online Multi-Scale Anomaly Detection via HNSW-Driven Graph Clustering and Dynamic Centroid Updates},<br \/>\r\nauthor = {Motomi Mizugai and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025)},<br \/>\r\npages = {1\u201314},<br \/>\r\npublisher = {Springer},<br \/>\r\naddress = {Kyoto, Japan},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('112','tp_bibtex')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Taichi Murayama; Kunihiro Miyazaki; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('102','tp_links')\" style=\"cursor:pointer;\">Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the International AAAI Conference on Web and Social Media, <\/span><span class=\"tp_pub_additional_pages\">pp. 1262\u20131294, <\/span><span class=\"tp_pub_additional_publisher\">AAAI Press, <\/span><span class=\"tp_pub_additional_address\">Copenhagen, Denmark, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_102\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('102','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_102\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B38_2025_LinguisticLandscapeofGenerative,<br \/>\r\ntitle = {Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages},<br \/>\r\nauthor = {Taichi Murayama and Kunihiro Miyazaki and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1609\/icwsm.v19i1.35872},<br \/>\r\ndoi = {10.1609\/icwsm.v19i1.35872},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the International AAAI Conference on Web and Social Media},<br \/>\r\nvolume = {19},<br \/>\r\nnumber = {1},<br \/>\r\npages = {1262\u20131294},<br \/>\r\npublisher = {AAAI Press},<br \/>\r\naddress = {Copenhagen, Denmark},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('102','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_102\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1609\/icwsm.v19i1.35872\" title=\"https:\/\/doi.org\/10.1609\/icwsm.v19i1.35872\" target=\"_blank\">https:\/\/doi.org\/10.1609\/icwsm.v19i1.35872<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1609\/icwsm.v19i1.35872\" title=\"Follow DOI:10.1609\/icwsm.v19i1.35872\" target=\"_blank\">doi:10.1609\/icwsm.v19i1.35872<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('102','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Shingo Higashiguchi; Yasuko Matsubara; Koki Kawabata; Taichi Murayama; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('103','tp_links')\" style=\"cursor:pointer;\">D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 460\u2013471, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Toronto, ON, Canada, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_103\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('103','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_103\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B39_2025_DTrackerModelingInterest,<br \/>\r\ntitle = {D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams},<br \/>\r\nauthor = {Shingo Higashiguchi and Yasuko Matsubara and Koki Kawabata and Taichi Murayama and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3690624.3709192},<br \/>\r\ndoi = {10.1145\/3690624.3709192},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025)},<br \/>\r\npages = {460\u2013471},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Toronto, ON, Canada},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('103','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_103\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3690624.3709192\" title=\"https:\/\/doi.org\/10.1145\/3690624.3709192\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3690624.3709192<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3690624.3709192\" title=\"Follow DOI:10.1145\/3690624.3709192\" target=\"_blank\">doi:10.1145\/3690624.3709192<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('103','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Naoki Chihara; Yasuko Matsubara; Ren Fujiwara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('104','tp_links')\" style=\"cursor:pointer;\">Modeling Time-evolving Causality over Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 153\u2013164, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Toronto, ON, Canada, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_104\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('104','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_104\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B40_2025_ModelingTimeevolvingCausality,<br \/>\r\ntitle = {Modeling Time-evolving Causality over Data Streams},<br \/>\r\nauthor = {Naoki Chihara and Yasuko Matsubara and Ren Fujiwara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3690624.3709283},<br \/>\r\ndoi = {10.1145\/3690624.3709283},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025)},<br \/>\r\npages = {153\u2013164},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Toronto, ON, Canada},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('104','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_104\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3690624.3709283\" title=\"https:\/\/doi.org\/10.1145\/3690624.3709283\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3690624.3709283<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3690624.3709283\" title=\"Follow DOI:10.1145\/3690624.3709283\" target=\"_blank\">doi:10.1145\/3690624.3709283<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('104','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Zheng Chen; Yasuko Matsubara; Yasushi Sakurai; Jimeng Sun<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('105','tp_links')\" style=\"cursor:pointer;\">Long-Term EEG Partitioning for Seizure Onset Detection<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the AAAI Conference on Artificial Intelligence, <\/span><span class=\"tp_pub_additional_pages\">pp. 14221\u201314229, <\/span><span class=\"tp_pub_additional_publisher\">AAAI Press, <\/span><span class=\"tp_pub_additional_address\">Philadelphia, Pennsylvania, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_105\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('105','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_105\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B41_2025_LongTermEEGPartitioning,<br \/>\r\ntitle = {Long-Term EEG Partitioning for Seizure Onset Detection},<br \/>\r\nauthor = {Zheng Chen and Yasuko Matsubara and Yasushi Sakurai and Jimeng Sun},<br \/>\r\nurl = {https:\/\/doi.org\/10.1609\/aaai.v39i13.33557},<br \/>\r\ndoi = {10.1609\/aaai.v39i13.33557},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},<br \/>\r\nvolume = {39},<br \/>\r\nnumber = {13},<br \/>\r\npages = {14221\u201314229},<br \/>\r\npublisher = {AAAI Press},<br \/>\r\naddress = {Philadelphia, Pennsylvania},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('105','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_105\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1609\/aaai.v39i13.33557\" title=\"https:\/\/doi.org\/10.1609\/aaai.v39i13.33557\" target=\"_blank\">https:\/\/doi.org\/10.1609\/aaai.v39i13.33557<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1609\/aaai.v39i13.33557\" title=\"Follow DOI:10.1609\/aaai.v39i13.33557\" target=\"_blank\">doi:10.1609\/aaai.v39i13.33557<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('105','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ren Fujiwara; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('106','tp_links')\" style=\"cursor:pointer;\">Modeling Latent Non-Linear Dynamical System over Time Series<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the AAAI Conference on Artificial Intelligence, <\/span><span class=\"tp_pub_additional_pages\">pp. 11663\u201311671, <\/span><span class=\"tp_pub_additional_publisher\">AAAI Press, <\/span><span class=\"tp_pub_additional_address\">Philadelphia, Pennsylvania, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_106\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('106','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_106\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B42_2025_ModelingLatentNonLinear,<br \/>\r\ntitle = {Modeling Latent Non-Linear Dynamical System over Time Series},<br \/>\r\nauthor = {Ren Fujiwara and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1609\/aaai.v39i11.33269},<br \/>\r\ndoi = {10.1609\/aaai.v39i11.33269},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},<br \/>\r\nvolume = {39},<br \/>\r\nnumber = {11},<br \/>\r\npages = {11663\u201311671},<br \/>\r\npublisher = {AAAI Press},<br \/>\r\naddress = {Philadelphia, Pennsylvania},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('106','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_106\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1609\/aaai.v39i11.33269\" title=\"https:\/\/doi.org\/10.1609\/aaai.v39i11.33269\" target=\"_blank\">https:\/\/doi.org\/10.1609\/aaai.v39i11.33269<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1609\/aaai.v39i11.33269\" title=\"Follow DOI:10.1609\/aaai.v39i11.33269\" target=\"_blank\">doi:10.1609\/aaai.v39i11.33269<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('106','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kota Nakamura; Koki Kawabata; Shungo Tanaka; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('107','tp_links')\" style=\"cursor:pointer;\">CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Companion Proceedings of the ACM Web Conference 2025 (WWW Companion 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 1214\u20131218, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Sydney, Australia, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_107\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('107','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_107\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B43_2025_CyberCScopeMiningSkewedTensor,<br \/>\r\ntitle = {CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems},<br \/>\r\nauthor = {Kota Nakamura and Koki Kawabata and Shungo Tanaka and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3701716.3715476},<br \/>\r\ndoi = {10.1145\/3701716.3715476},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Companion Proceedings of the ACM Web Conference 2025 (WWW Companion 2025)},<br \/>\r\npages = {1214\u20131218},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Sydney, Australia},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('107','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_107\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3701716.3715476\" title=\"https:\/\/doi.org\/10.1145\/3701716.3715476\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3701716.3715476<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3701716.3715476\" title=\"Follow DOI:10.1145\/3701716.3715476\" target=\"_blank\">doi:10.1145\/3701716.3715476<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('107','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ziwei Yang; Zheng Chen; Xin Liu; Rikuto Kotoge; Peng Chen; Yasuko Matsubara; Yasushi Sakurai; Jimeng Sun<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('108','tp_links')\" style=\"cursor:pointer;\">GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">The Thirteenth International Conference on Learning Representations (ICLR 2025), <\/span><span class=\"tp_pub_additional_address\">Singapore, <\/span><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (ICLR 2025 Oral)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_108\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('108','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_108\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B44_2025_GeSubNetGeneInteractionInference,<br \/>\r\ntitle = {GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation},<br \/>\r\nauthor = {Ziwei Yang and Zheng Chen and Xin Liu and Rikuto Kotoge and Peng Chen and Yasuko Matsubara and Yasushi Sakurai and Jimeng Sun},<br \/>\r\nurl = {https:\/\/openreview.net\/forum?id=ja4rpheN2n},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {The Thirteenth International Conference on Learning Representations (ICLR 2025)},<br \/>\r\naddress = {Singapore},<br \/>\r\nnote = {ICLR 2025 Oral},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('108','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_108\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/openreview.net\/forum?id=ja4rpheN2n\" title=\"https:\/\/openreview.net\/forum?id=ja4rpheN2n\" target=\"_blank\">https:\/\/openreview.net\/forum?id=ja4rpheN2n<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('108','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kohei Obata; Taichi Murayama; Zheng Chen; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('109','tp_links')\" style=\"cursor:pointer;\">Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 172\u2013184, <\/span><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_address\">Sydney, Australia, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_109\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('109','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_109\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B45_2025_DisentangledModeSpecificRepresentati,<br \/>\r\ntitle = {Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning},<br \/>\r\nauthor = {Kohei Obata and Taichi Murayama and Zheng Chen and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-981-96-8295-9_12},<br \/>\r\ndoi = {10.1007\/978-981-96-8295-9_12},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2025)},<br \/>\r\nvolume = {15875},<br \/>\r\npages = {172\u2013184},<br \/>\r\npublisher = {Springer},<br \/>\r\naddress = {Sydney, Australia},<br \/>\r\nseries = {Lecture Notes in Computer Science},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('109','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_109\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-981-96-8295-9_12\" title=\"https:\/\/doi.org\/10.1007\/978-981-96-8295-9_12\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-981-96-8295-9_12<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-981-96-8295-9_12\" title=\"Follow DOI:10.1007\/978-981-96-8295-9_12\" target=\"_blank\">doi:10.1007\/978-981-96-8295-9_12<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('109','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('110','tp_links')\" style=\"cursor:pointer;\">MicroAdapt: Self-Evolutionary Dynamic Modeling Algorithms for Time-evolving Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 2114\u20132125, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Toronto, ON, Canada, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_110\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('110','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_110\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B46_2025_MicroAdaptSelfEvolutionaryDynamic,<br \/>\r\ntitle = {MicroAdapt: Self-Evolutionary Dynamic Modeling Algorithms for Time-evolving Data Streams},<br \/>\r\nauthor = {Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3711896.3737048},<br \/>\r\ndoi = {10.1145\/3711896.3737048},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025)},<br \/>\r\npages = {2114\u20132125},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Toronto, ON, Canada},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('110','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_110\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3711896.3737048\" title=\"https:\/\/doi.org\/10.1145\/3711896.3737048\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3711896.3737048<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3711896.3737048\" title=\"Follow DOI:10.1145\/3711896.3737048\" target=\"_blank\">doi:10.1145\/3711896.3737048<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('110','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kohei Obata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('111','tp_links')\" style=\"cursor:pointer;\">Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025), <\/span><span class=\"tp_pub_additional_pages\">pp. 2198\u20132209, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Toronto, ON, Canada, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_111\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('111','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_111\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B47_2025_RobustandExplainableDetector,<br \/>\r\ntitle = {Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies},<br \/>\r\nauthor = {Kohei Obata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3711896.3737110},<br \/>\r\ndoi = {10.1145\/3711896.3737110},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025)},<br \/>\r\npages = {2198\u20132209},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Toronto, ON, Canada},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('111','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_111\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3711896.3737110\" title=\"https:\/\/doi.org\/10.1145\/3711896.3737110\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3711896.3737110<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3711896.3737110\" title=\"Follow DOI:10.1145\/3711896.3737110\" target=\"_blank\">doi:10.1145\/3711896.3737110<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('111','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Satoshi Kayano<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('113','tp_links')\" style=\"cursor:pointer;\">Entropy-Based Anomaly Detection in Evolving Graph Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">VLDB 2025 PhD Workshop, <\/span><span class=\"tp_pub_additional_pages\">pp. 1\u20134, <\/span><span class=\"tp_pub_additional_publisher\">VLDB Endowment, <\/span><span class=\"tp_pub_additional_address\">London, United Kingdom, <\/span><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (Supervised by Koki Kawabata, Yasuko Matsubara, and Yasushi Sakurai)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_113\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('113','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_113\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B49_2025_EntropyBasedAnomalyDetection,<br \/>\r\ntitle = {Entropy-Based Anomaly Detection in Evolving Graph Streams},<br \/>\r\nauthor = {Satoshi Kayano},<br \/>\r\nurl = {https:\/\/www.vldb.org\/2025\/Workshops\/VLDB-Workshops-2025\/PhD\/PhD25_7.pdf},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {VLDB 2025 PhD Workshop},<br \/>\r\npages = {1\u20134},<br \/>\r\npublisher = {VLDB Endowment},<br \/>\r\naddress = {London, United Kingdom},<br \/>\r\nnote = {Supervised by Koki Kawabata, Yasuko Matsubara, and Yasushi Sakurai},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('113','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_113\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-file-pdf\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.vldb.org\/2025\/Workshops\/VLDB-Workshops-2025\/PhD\/PhD25_7.pdf\" title=\"https:\/\/www.vldb.org\/2025\/Workshops\/VLDB-Workshops-2025\/PhD\/PhD25_7.pdf\" target=\"_blank\">https:\/\/www.vldb.org\/2025\/Workshops\/VLDB-Workshops-2025\/PhD\/PhD25_7.pdf<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('113','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Rikuto Kotoge; Zheng Chen; Tasuku Kimura; Yasuko Matsubara; Takufumi Yanagisawa; Haruhiko Kishima; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('114','tp_links')\" style=\"cursor:pointer;\">EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Advances in Neural Information Processing Systems 38 (NeurIPS 2025), <\/span><span class=\"tp_pub_additional_publisher\">NeurIPS Foundation, <\/span><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (NeurIPS 2025 Spotlight)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_114\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('114','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_114\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B50_2025_EvoBrainDynamicMultichannel,<br \/>\r\ntitle = {EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks},<br \/>\r\nauthor = {Rikuto Kotoge and Zheng Chen and Tasuku Kimura and Yasuko Matsubara and Takufumi Yanagisawa and Haruhiko Kishima and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.52202\/085713-4837},<br \/>\r\ndoi = {10.52202\/085713-4837},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {Advances in Neural Information Processing Systems 38 (NeurIPS 2025)},<br \/>\r\nvolume = {38},<br \/>\r\npublisher = {NeurIPS Foundation},<br \/>\r\nnote = {NeurIPS 2025 Spotlight},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('114','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_114\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.52202\/085713-4837\" title=\"https:\/\/doi.org\/10.52202\/085713-4837\" target=\"_blank\">https:\/\/doi.org\/10.52202\/085713-4837<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.52202\/085713-4837\" title=\"Follow DOI:10.52202\/085713-4837\" target=\"_blank\">doi:10.52202\/085713-4837<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('114','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Koki Yoshida; Zheng Chen; Rikuto Kotoge; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('115','tp_links')\" style=\"cursor:pointer;\">Learning Structured Sleep Transitions with Sequential EEG Foundation Models<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">NeurIPS 2025 Workshop on Foundation Models for the Brain and Body (BrainBodyFM), <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_115\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('115','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_115\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B51_2025_LearningStructuredSleepTransitions,<br \/>\r\ntitle = {Learning Structured Sleep Transitions with Sequential EEG Foundation Models},<br \/>\r\nauthor = {Koki Yoshida and Zheng Chen and Rikuto Kotoge and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/openreview.net\/forum?id=uqHvhm6BQM},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {NeurIPS 2025 Workshop on Foundation Models for the Brain and Body (BrainBodyFM)},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('115','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_115\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/openreview.net\/forum?id=uqHvhm6BQM\" title=\"https:\/\/openreview.net\/forum?id=uqHvhm6BQM\" target=\"_blank\">https:\/\/openreview.net\/forum?id=uqHvhm6BQM<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('115','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yuka Tamura; Yasuko Matsubara; Tasuku Kimura; Ren Fujiwara; Tsuyoshi Sekitani; Sakaaki Machimura; Masayuki Endo; Tadashi Kimura; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('116','tp_links')\" style=\"cursor:pointer;\">Discovery of Latent Dynamical Patterns in Biomedical Data<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">IEEE International Conference on Data Mining, ICDM-W, DMBIH, <\/span><span class=\"tp_pub_additional_pages\">pp. 802\u2013811, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_116\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('116','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_116\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B52_2025_DiscoveryofLatentDynamical,<br \/>\r\ntitle = {Discovery of Latent Dynamical Patterns in Biomedical Data},<br \/>\r\nauthor = {Yuka Tamura and Yasuko Matsubara and Tasuku Kimura and Ren Fujiwara and Tsuyoshi Sekitani and Sakaaki Machimura and Masayuki Endo and Tadashi Kimura and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/ICDMW69685.2025.00096},<br \/>\r\ndoi = {10.1109\/ICDMW69685.2025.00096},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nbooktitle = {IEEE International Conference on Data Mining, ICDM-W, DMBIH},<br \/>\r\npages = {802\u2013811},<br \/>\r\npublisher = {IEEE},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('116','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_116\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/ICDMW69685.2025.00096\" title=\"https:\/\/doi.org\/10.1109\/ICDMW69685.2025.00096\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICDMW69685.2025.00096<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICDMW69685.2025.00096\" title=\"Follow DOI:10.1109\/ICDMW69685.2025.00096\" target=\"_blank\">doi:10.1109\/ICDMW69685.2025.00096<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('116','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2024\">2024<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Rikuto Kotoge; Zheng Chen; Tasuku Kimura; Yasuko Matsubara; Takufumi Yanagisawa; Haruhiko Kishima; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('101','tp_links')\" style=\"cursor:pointer;\">SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2024 IEEE International Conference on Data Mining (ICDM 2024), <\/span><span class=\"tp_pub_additional_pages\">pp. 741\u2013746, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_address\">Abu Dhabi, UAE, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_101\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('101','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_101\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B37_2024_SplitSEEASplittableSelf,<br \/>\r\ntitle = {SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning},<br \/>\r\nauthor = {Rikuto Kotoge and Zheng Chen and Tasuku Kimura and Yasuko Matsubara and Takufumi Yanagisawa and Haruhiko Kishima and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/ICDM59182.2024.00086},<br \/>\r\ndoi = {10.1109\/ICDM59182.2024.00086},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nbooktitle = {2024 IEEE International Conference on Data Mining (ICDM 2024)},<br \/>\r\npages = {741\u2013746},<br \/>\r\npublisher = {IEEE},<br \/>\r\naddress = {Abu Dhabi, UAE},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('101','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_101\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/ICDM59182.2024.00086\" title=\"https:\/\/doi.org\/10.1109\/ICDM59182.2024.00086\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICDM59182.2024.00086<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICDM59182.2024.00086\" title=\"Follow DOI:10.1109\/ICDM59182.2024.00086\" target=\"_blank\">doi:10.1109\/ICDM59182.2024.00086<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('101','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Xihao Piao; Zheng Chen; Taichi Murayama; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('100','tp_links')\" style=\"cursor:pointer;\">Fredformer: Frequency Debiased Transformer for Time Series Forecasting<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), <\/span><span class=\"tp_pub_additional_pages\">pp. 2400\u20132410, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Barcelona, Spain, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_100\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('100','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_100\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B36_2024_FredformerFrequencyDebiasedTransform,<br \/>\r\ntitle = {Fredformer: Frequency Debiased Transformer for Time Series Forecasting},<br \/>\r\nauthor = {Xihao Piao and Zheng Chen and Taichi Murayama and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3637528.3671928},<br \/>\r\ndoi = {10.1145\/3637528.3671928},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nbooktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)},<br \/>\r\npages = {2400\u20132410},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Barcelona, Spain},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('100','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_100\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3637528.3671928\" title=\"https:\/\/doi.org\/10.1145\/3637528.3671928\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3637528.3671928<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3637528.3671928\" title=\"Follow DOI:10.1145\/3637528.3671928\" target=\"_blank\">doi:10.1145\/3637528.3671928<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('100','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kohei Obata; Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('99','tp_links')\" style=\"cursor:pointer;\">Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), <\/span><span class=\"tp_pub_additional_pages\">pp. 2296\u20132306, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Barcelona, Spain, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_99\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('99','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_99\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B35_2024_MiningofSwitchingSparse,<br \/>\r\ntitle = {Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series},<br \/>\r\nauthor = {Kohei Obata and Koki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3637528.3671760},<br \/>\r\ndoi = {10.1145\/3637528.3671760},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nbooktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)},<br \/>\r\npages = {2296\u20132306},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Barcelona, Spain},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('99','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_99\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3637528.3671760\" title=\"https:\/\/doi.org\/10.1145\/3637528.3671760\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3637528.3671760<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3637528.3671760\" title=\"Follow DOI:10.1145\/3637528.3671760\" target=\"_blank\">doi:10.1145\/3637528.3671760<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('99','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kohei Obata; Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('98','tp_links')\" style=\"cursor:pointer;\">Dynamic Multi-Network Mining of Tensor Time Series<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the ACM Web Conference 2024 (WWW 2024), <\/span><span class=\"tp_pub_additional_pages\">pp. 4117\u20134127, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Singapore, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_98\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('98','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_98\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B34_2024_DynamicMultiNetworkMining,<br \/>\r\ntitle = {Dynamic Multi-Network Mining of Tensor Time Series},<br \/>\r\nauthor = {Kohei Obata and Koki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3589334.3645461},<br \/>\r\ndoi = {10.1145\/3589334.3645461},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nbooktitle = {Proceedings of the ACM Web Conference 2024 (WWW 2024)},<br \/>\r\npages = {4117\u20134127},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Singapore},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('98','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_98\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3589334.3645461\" title=\"https:\/\/doi.org\/10.1145\/3589334.3645461\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3589334.3645461<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3589334.3645461\" title=\"Follow DOI:10.1145\/3589334.3645461\" target=\"_blank\">doi:10.1145\/3589334.3645461<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('98','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2023\">2023<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ziwei Yang; Zheng Chen; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('96','tp_links')\" style=\"cursor:pointer;\">MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023), <\/span><span class=\"tp_pub_additional_pages\">pp. 2895\u20132905, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Birmingham, UK, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_96\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('96','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_96\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B32_2023_MoCLIMTowardsAccurateCancer,<br \/>\r\ntitle = {MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling},<br \/>\r\nauthor = {Ziwei Yang and Zheng Chen and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3583780.3614970},<br \/>\r\ndoi = {10.1145\/3583780.3614970},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nbooktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023)},<br \/>\r\npages = {2895\u20132905},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Birmingham, UK},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('96','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_96\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3583780.3614970\" title=\"https:\/\/doi.org\/10.1145\/3583780.3614970\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3583780.3614970<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3583780.3614970\" title=\"Follow DOI:10.1145\/3583780.3614970\" target=\"_blank\">doi:10.1145\/3583780.3614970<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('96','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Honglin Shu; Pei Gao; Lingwei Zhu; Zheng Chen; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('97','tp_links')\" style=\"cursor:pointer;\">Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">International Conference on Advanced Data Mining and Applications (ADMA 2023), <\/span><span class=\"tp_pub_additional_pages\">pp. 103\u2013117, <\/span><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_address\">Shenyang, China, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_97\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('97','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_97\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B33_2023_DrugsResistanceAnalysisfrom,<br \/>\r\ntitle = {Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation},<br \/>\r\nauthor = {Honglin Shu and Pei Gao and Lingwei Zhu and Zheng Chen and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-031-46671-7_8},<br \/>\r\ndoi = {10.1007\/978-3-031-46671-7_8},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nbooktitle = {International Conference on Advanced Data Mining and Applications (ADMA 2023)},<br \/>\r\npages = {103\u2013117},<br \/>\r\npublisher = {Springer},<br \/>\r\naddress = {Shenyang, China},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('97','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_97\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-031-46671-7_8\" title=\"https:\/\/doi.org\/10.1007\/978-3-031-46671-7_8\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-46671-7_8<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-46671-7_8\" title=\"Follow DOI:10.1007\/978-3-031-46671-7_8\" target=\"_blank\">doi:10.1007\/978-3-031-46671-7_8<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('97','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Zheng Zhang; Fu Shimizu; Yasuko Matsubara; Aiji Suetake; Chuantong Chen; Yasushi Sakurai; Katsuaki Suganuma<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('95','tp_links')\" style=\"cursor:pointer;\">Condition monitoring of SiC power assembly by using time-series analysis of acoustic emission during power cycling tests<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2023 24th International Conference on Electronic Packaging Technology (ICEPT), <\/span><span class=\"tp_pub_additional_pages\">pp. 1\u20135, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_address\">Xinjiang, China, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_95\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('95','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_95\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B31_2023_ConditionmonitoringofSiC,<br \/>\r\ntitle = {Condition monitoring of SiC power assembly by using time-series analysis of acoustic emission during power cycling tests},<br \/>\r\nauthor = {Zheng Zhang and Fu Shimizu and Yasuko Matsubara and Aiji Suetake and Chuantong Chen and Yasushi Sakurai and Katsuaki Suganuma},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/ICEPT59018.2023.10492346},<br \/>\r\ndoi = {10.1109\/ICEPT59018.2023.10492346},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nbooktitle = {2023 24th International Conference on Electronic Packaging Technology (ICEPT)},<br \/>\r\npages = {1\u20135},<br \/>\r\npublisher = {IEEE},<br \/>\r\naddress = {Xinjiang, China},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('95','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_95\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/ICEPT59018.2023.10492346\" title=\"https:\/\/doi.org\/10.1109\/ICEPT59018.2023.10492346\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICEPT59018.2023.10492346<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICEPT59018.2023.10492346\" title=\"Follow DOI:10.1109\/ICEPT59018.2023.10492346\" target=\"_blank\">doi:10.1109\/ICEPT59018.2023.10492346<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('95','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Taichi Murayama; Akira Matsui; Kunihiro Miyazaki; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('94','tp_links')\" style=\"cursor:pointer;\">The Chance of Winning Election Impacts on Social Media Strategy<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the International AAAI Conference on Web and Social Media, <\/span><span class=\"tp_pub_additional_pages\">pp. 674\u2013685, <\/span><span class=\"tp_pub_additional_publisher\">AAAI Press, <\/span><span class=\"tp_pub_additional_address\">Limassol, Cyprus, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_94\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('94','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_94\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B30_2023_TheChanceofWinning,<br \/>\r\ntitle = {The Chance of Winning Election Impacts on Social Media Strategy},<br \/>\r\nauthor = {Taichi Murayama and Akira Matsui and Kunihiro Miyazaki and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1609\/icwsm.v17i1.22178},<br \/>\r\ndoi = {10.1609\/icwsm.v17i1.22178},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nbooktitle = {Proceedings of the International AAAI Conference on Web and Social Media},<br \/>\r\nvolume = {17},<br \/>\r\nnumber = {1},<br \/>\r\npages = {674\u2013685},<br \/>\r\npublisher = {AAAI Press},<br \/>\r\naddress = {Limassol, Cyprus},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('94','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_94\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1609\/icwsm.v17i1.22178\" title=\"https:\/\/doi.org\/10.1609\/icwsm.v17i1.22178\" target=\"_blank\">https:\/\/doi.org\/10.1609\/icwsm.v17i1.22178<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1609\/icwsm.v17i1.22178\" title=\"Follow DOI:10.1609\/icwsm.v17i1.22178\" target=\"_blank\">doi:10.1609\/icwsm.v17i1.22178<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('94','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('93','tp_links')\" style=\"cursor:pointer;\">Modeling Dynamic Interactions over Tensor Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the ACM Web Conference 2023 (WWW 2023), <\/span><span class=\"tp_pub_additional_pages\">pp. 1793\u20131803, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Austin, Texas, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_93\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('93','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_93\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B29_2023_ModelingDynamicInteractionsover,<br \/>\r\ntitle = {Modeling Dynamic Interactions over Tensor Streams},<br \/>\r\nauthor = {Koki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3543507.3583458},<br \/>\r\ndoi = {10.1145\/3543507.3583458},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nbooktitle = {Proceedings of the ACM Web Conference 2023 (WWW 2023)},<br \/>\r\npages = {1793\u20131803},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Austin, Texas},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('93','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_93\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3543507.3583458\" title=\"https:\/\/doi.org\/10.1145\/3543507.3583458\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3543507.3583458<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3543507.3583458\" title=\"Follow DOI:10.1145\/3543507.3583458\" target=\"_blank\">doi:10.1145\/3543507.3583458<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('93','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kota Nakamura; Yasuko Matsubara; Koki Kawabata; Yuhei Umeda; Yuichiro Wada; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('92','tp_links')\" style=\"cursor:pointer;\">Fast and Multi-aspect Mining of Complex Time-stamped Event Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the ACM Web Conference 2023 (WWW 2023), <\/span><span class=\"tp_pub_additional_pages\">pp. 1638\u20131649, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Austin, Texas, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_92\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('92','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_92\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B28_2023_FastandMultiaspect,<br \/>\r\ntitle = {Fast and Multi-aspect Mining of Complex Time-stamped Event Streams},<br \/>\r\nauthor = {Kota Nakamura and Yasuko Matsubara and Koki Kawabata and Yuhei Umeda and Yuichiro Wada and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3543507.3583370},<br \/>\r\ndoi = {10.1145\/3543507.3583370},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nbooktitle = {Proceedings of the ACM Web Conference 2023 (WWW 2023)},<br \/>\r\npages = {1638\u20131649},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Austin, Texas},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('92','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_92\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3543507.3583370\" title=\"https:\/\/doi.org\/10.1145\/3543507.3583370\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3543507.3583370<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3543507.3583370\" title=\"Follow DOI:10.1145\/3543507.3583370\" target=\"_blank\">doi:10.1145\/3543507.3583370<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('92','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2022\">2022<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Akihiro Tanabe; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('91','tp_links')\" style=\"cursor:pointer;\">Partial Discharge Detection for Underground Transmission Lines Using Nonnegative Matrix Factorization<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">The 2022 IEEE International Conference on Big Data, BTSD workshop, <\/span><span class=\"tp_pub_additional_pages\">pp. 3447\u20133454, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_address\">Hybrid Conference, Osaka, Japan, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_91\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('91','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_91\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B27_2022_PartialDischargeDetectionfor,<br \/>\r\ntitle = {Partial Discharge Detection for Underground Transmission Lines Using Nonnegative Matrix Factorization},<br \/>\r\nauthor = {Akihiro Tanabe and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/BigData55660.2022.10020673},<br \/>\r\ndoi = {10.1109\/BigData55660.2022.10020673},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\nbooktitle = {The 2022 IEEE International Conference on Big Data, BTSD workshop},<br \/>\r\npages = {3447\u20133454},<br \/>\r\npublisher = {IEEE},<br \/>\r\naddress = {Hybrid Conference, Osaka, Japan},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('91','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_91\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/BigData55660.2022.10020673\" title=\"https:\/\/doi.org\/10.1109\/BigData55660.2022.10020673\" target=\"_blank\">https:\/\/doi.org\/10.1109\/BigData55660.2022.10020673<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/BigData55660.2022.10020673\" title=\"Follow DOI:10.1109\/BigData55660.2022.10020673\" target=\"_blank\">doi:10.1109\/BigData55660.2022.10020673<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('91','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Ren Fujiwara; Yasuko Matsubara; Tasuku Kimura; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('90','tp_links')\" style=\"cursor:pointer;\">C-Cast: A Real-Time Forecasting Model for a Controlled Sequence<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), <\/span><span class=\"tp_pub_additional_pages\">pp. 5112\u20135115, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Hybrid Conference, Hosted in Atlanta, Georgia, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_90\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('90','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_90\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B26_2022_CCastAReal,<br \/>\r\ntitle = {C-Cast: A Real-Time Forecasting Model for a Controlled Sequence},<br \/>\r\nauthor = {Ren Fujiwara and Yasuko Matsubara and Tasuku Kimura and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3511808.3557817},<br \/>\r\ndoi = {10.1145\/3511808.3557817},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\nbooktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022)},<br \/>\r\npages = {5112\u20135115},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Hybrid Conference, Hosted in Atlanta, Georgia},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('90','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_90\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3511808.3557817\" title=\"https:\/\/doi.org\/10.1145\/3511808.3557817\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3511808.3557817<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3511808.3557817\" title=\"Follow DOI:10.1145\/3511808.3557817\" target=\"_blank\">doi:10.1145\/3511808.3557817<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('90','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kohei Obata; Yasuko Matsubara; Koki Kawabata; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('89','tp_links')\" style=\"cursor:pointer;\">Automatic Time-Series Clustering via Network Inference<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">48th International Conference on Very Large Data Bases (VLDB) PhD workshop, <\/span><span class=\"tp_pub_additional_pages\">pp. 1\u20136, <\/span><span class=\"tp_pub_additional_publisher\">VLDB Endowment, <\/span><span class=\"tp_pub_additional_address\">Sydney, Australia, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_89\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('89','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_89\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B25_2022_AutomaticTimeSeriesClustering,<br \/>\r\ntitle = {Automatic Time-Series Clustering via Network Inference},<br \/>\r\nauthor = {Kohei Obata and Yasuko Matsubara and Koki Kawabata and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/ceur-ws.org\/Vol-3186\/},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\nbooktitle = {48th International Conference on Very Large Data Bases (VLDB) PhD workshop},<br \/>\r\npages = {1\u20136},<br \/>\r\npublisher = {VLDB Endowment},<br \/>\r\naddress = {Sydney, Australia},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('89','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_89\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/ceur-ws.org\/Vol-3186\/\" title=\"https:\/\/ceur-ws.org\/Vol-3186\/\" target=\"_blank\">https:\/\/ceur-ws.org\/Vol-3186\/<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('89','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Taichi Murayama; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('88','tp_links')\" style=\"cursor:pointer;\">Mining Reaction and Diffusion Dynamics in Social Activities<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), <\/span><span class=\"tp_pub_additional_pages\">pp. 1521\u20131531, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Hybrid Conference, Hosted in Atlanta, Georgia, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_88\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('88','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_88\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B24_2022_MiningReactionandDiffusion,<br \/>\r\ntitle = {Mining Reaction and Diffusion Dynamics in Social Activities},<br \/>\r\nauthor = {Taichi Murayama and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3511808.3557396},<br \/>\r\ndoi = {10.1145\/3511808.3557396},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\nbooktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022)},<br \/>\r\npages = {1521\u20131531},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Hybrid Conference, Hosted in Atlanta, Georgia},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('88','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_88\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3511808.3557396\" title=\"https:\/\/doi.org\/10.1145\/3511808.3557396\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3511808.3557396<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3511808.3557396\" title=\"Follow DOI:10.1145\/3511808.3557396\" target=\"_blank\">doi:10.1145\/3511808.3557396<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('88','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Tasuku Kimura; Yasuko Matsubara; Koki Kawabata; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('87','tp_links')\" style=\"cursor:pointer;\">Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), <\/span><span class=\"tp_pub_additional_pages\">pp. 3157\u20133167, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Washington DC, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_87\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('87','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_87\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B23_2022_FastMiningandForecasting,<br \/>\r\ntitle = {Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams},<br \/>\r\nauthor = {Tasuku Kimura and Yasuko Matsubara and Koki Kawabata and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3534678.3539078},<br \/>\r\ndoi = {10.1145\/3534678.3539078},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\nbooktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)},<br \/>\r\npages = {3157\u20133167},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Washington DC},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('87','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_87\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3534678.3539078\" title=\"https:\/\/doi.org\/10.1145\/3534678.3539078\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3534678.3539078<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3534678.3539078\" title=\"Follow DOI:10.1145\/3534678.3539078\" target=\"_blank\">doi:10.1145\/3534678.3539078<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('87','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2020\">2020<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Koki Kawabata; Yasuko Matsubara; Takato Honda; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('86','tp_links')\" style=\"cursor:pointer;\">Non-Linear Mining of Social Activities in Tensor Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020), <\/span><span class=\"tp_pub_additional_pages\">pp. 2093\u20132102, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Virtual Event, California, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_86\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('86','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_86\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B22_2020_NonLinearMiningof,<br \/>\r\ntitle = {Non-Linear Mining of Social Activities in Tensor Streams},<br \/>\r\nauthor = {Koki Kawabata and Yasuko Matsubara and Takato Honda and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3394486.3403260},<br \/>\r\ndoi = {10.1145\/3394486.3403260},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\nbooktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020)},<br \/>\r\npages = {2093\u20132102},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Virtual Event, California},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('86','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_86\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3394486.3403260\" title=\"https:\/\/doi.org\/10.1145\/3394486.3403260\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3394486.3403260<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3394486.3403260\" title=\"Follow DOI:10.1145\/3394486.3403260\" target=\"_blank\">doi:10.1145\/3394486.3403260<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('86','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2019\">2019<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Takato Honda; Yasuko Matsubara; Ryo Neyama; Mutsumi Abe; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('85','tp_links')\" style=\"cursor:pointer;\">Multi-Aspect Mining of Complex Sensor Sequences<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2019 IEEE International Conference on Data Mining (ICDM 2019), <\/span><span class=\"tp_pub_additional_pages\">pp. 299\u2013308, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_address\">Beijing, China, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_85\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('85','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_85\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B21_2019_MultiAspectMiningof,<br \/>\r\ntitle = {Multi-Aspect Mining of Complex Sensor Sequences},<br \/>\r\nauthor = {Takato Honda and Yasuko Matsubara and Ryo Neyama and Mutsumi Abe and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/ICDM.2019.00040},<br \/>\r\ndoi = {10.1109\/ICDM.2019.00040},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nbooktitle = {2019 IEEE International Conference on Data Mining (ICDM 2019)},<br \/>\r\npages = {299\u2013308},<br \/>\r\npublisher = {IEEE},<br \/>\r\naddress = {Beijing, China},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('85','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_85\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/ICDM.2019.00040\" title=\"https:\/\/doi.org\/10.1109\/ICDM.2019.00040\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICDM.2019.00040<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICDM.2019.00040\" title=\"Follow DOI:10.1109\/ICDM.2019.00040\" target=\"_blank\">doi:10.1109\/ICDM.2019.00040<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('85','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('84','tp_links')\" style=\"cursor:pointer;\">Automatic Sequential Pattern Mining in Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), <\/span><span class=\"tp_pub_additional_pages\">pp. 1733\u20131742, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Beijing, China, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_84\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('84','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_84\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B20_2019_AutomaticSequentialPatternMining,<br \/>\r\ntitle = {Automatic Sequential Pattern Mining in Data Streams},<br \/>\r\nauthor = {Koki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3357384.3358002},<br \/>\r\ndoi = {10.1145\/3357384.3358002},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nbooktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019)},<br \/>\r\npages = {1733\u20131742},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Beijing, China},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('84','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_84\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3357384.3358002\" title=\"https:\/\/doi.org\/10.1145\/3357384.3358002\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3357384.3358002<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3357384.3358002\" title=\"Follow DOI:10.1145\/3357384.3358002\" target=\"_blank\">doi:10.1145\/3357384.3358002<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('84','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('83','tp_links')\" style=\"cursor:pointer;\">Dynamic Modeling and Forecasting of Time-evolving Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), <\/span><span class=\"tp_pub_additional_pages\">pp. 458\u2013468, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Anchorage, Alaska, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_83\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('83','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_83\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B19_2019_DynamicModelingandForecasting,<br \/>\r\ntitle = {Dynamic Modeling and Forecasting of Time-evolving Data Streams},<br \/>\r\nauthor = {Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3292500.3330947},<br \/>\r\ndoi = {10.1145\/3292500.3330947},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nbooktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019)},<br \/>\r\npages = {458\u2013468},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Anchorage, Alaska},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('83','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_83\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3292500.3330947\" title=\"https:\/\/doi.org\/10.1145\/3292500.3330947\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3292500.3330947<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3292500.3330947\" title=\"Follow DOI:10.1145\/3292500.3330947\" target=\"_blank\">doi:10.1145\/3292500.3330947<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('83','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2018\">2018<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kouki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('81','tp_links')\" style=\"cursor:pointer;\">Data Stream Analysis of Online Activities<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">IEEE iThings\/GreenCom\/CPSCom\/SmartData 2018, <\/span><span class=\"tp_pub_additional_pages\">pp. 925\u2013926, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_address\">Halifax, NS, Canada, <\/span><span class=\"tp_pub_additional_year\">2018<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_81\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('81','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_81\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B17_2018_DataStreamAnalysisof,<br \/>\r\ntitle = {Data Stream Analysis of Online Activities},<br \/>\r\nauthor = {Kouki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/Cybermatics_2018.2018.00175},<br \/>\r\ndoi = {10.1109\/Cybermatics_2018.2018.00175},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\nbooktitle = {IEEE iThings\/GreenCom\/CPSCom\/SmartData 2018},<br \/>\r\npages = {925\u2013926},<br \/>\r\npublisher = {IEEE},<br \/>\r\naddress = {Halifax, NS, Canada},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('81','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_81\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/Cybermatics_2018.2018.00175\" title=\"https:\/\/doi.org\/10.1109\/Cybermatics_2018.2018.00175\" target=\"_blank\">https:\/\/doi.org\/10.1109\/Cybermatics_2018.2018.00175<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/Cybermatics_2018.2018.00175\" title=\"Follow DOI:10.1109\/Cybermatics_2018.2018.00175\" target=\"_blank\">doi:10.1109\/Cybermatics_2018.2018.00175<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('81','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Kouki Kawabata; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('80','tp_links')\" style=\"cursor:pointer;\">StreamScope: Automatic Pattern Discovery over Data Streams<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM@SIGMOD 2018), <\/span><span class=\"tp_pub_additional_pages\">pp. 5:1\u20135:8, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">Houston, TX, <\/span><span class=\"tp_pub_additional_year\">2018<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_80\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('80','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_80\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B16_2018_StreamScopeAutomaticPatternDiscovery,<br \/>\r\ntitle = {StreamScope: Automatic Pattern Discovery over Data Streams},<br \/>\r\nauthor = {Kouki Kawabata and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3211954.3211959},<br \/>\r\ndoi = {10.1145\/3211954.3211959},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\nbooktitle = {Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM@SIGMOD 2018)},<br \/>\r\npages = {5:1\u20135:8},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {Houston, TX},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('80','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_80\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3211954.3211959\" title=\"https:\/\/doi.org\/10.1145\/3211954.3211959\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3211954.3211959<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3211954.3211959\" title=\"Follow DOI:10.1145\/3211954.3211959\" target=\"_blank\">doi:10.1145\/3211954.3211959<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('80','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Takato Honda; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('82','tp_links')\" style=\"cursor:pointer;\">Automatic Mining of Large IoT Sensor Tensor<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">IEEE International Conference on Data Mining, ICDM Workshops (ICDMW), <\/span><span class=\"tp_pub_additional_pages\">pp. 1503\u20131504, <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_address\">Singapore, <\/span><span class=\"tp_pub_additional_year\">2018<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_82\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('82','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_82\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B18_2018_AutomaticMiningofLarge,<br \/>\r\ntitle = {Automatic Mining of Large IoT Sensor Tensor},<br \/>\r\nauthor = {Takato Honda and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/ICDMW.2018.00219},<br \/>\r\ndoi = {10.1109\/ICDMW.2018.00219},<br \/>\r\nyear  = {2018},<br \/>\r\ndate = {2018-01-01},<br \/>\r\nbooktitle = {IEEE International Conference on Data Mining, ICDM Workshops (ICDMW)},<br \/>\r\npages = {1503\u20131504},<br \/>\r\npublisher = {IEEE},<br \/>\r\naddress = {Singapore},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('82','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_82\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/ICDMW.2018.00219\" title=\"https:\/\/doi.org\/10.1109\/ICDMW.2018.00219\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICDMW.2018.00219<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICDMW.2018.00219\" title=\"Follow DOI:10.1109\/ICDMW.2018.00219\" target=\"_blank\">doi:10.1109\/ICDMW.2018.00219<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('82','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2017\">2017<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Thinh Minh Do; Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('79','tp_links')\" style=\"cursor:pointer;\">Automatic and Effective Mining of Coevolving Online Activities<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2017), <\/span><span class=\"tp_pub_additional_pages\">pp. 233\u2013246, <\/span><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_address\">Jeju, South Korea, <\/span><span class=\"tp_pub_additional_year\">2017<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_79\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('79','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_79\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B15_2017_AutomaticandEffectiveMining,<br \/>\r\ntitle = {Automatic and Effective Mining of Coevolving Online Activities},<br \/>\r\nauthor = {Thinh Minh Do and Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-319-57529-2_19},<br \/>\r\ndoi = {10.1007\/978-3-319-57529-2_19},<br \/>\r\nyear  = {2017},<br \/>\r\ndate = {2017-01-01},<br \/>\r\nbooktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2017)},<br \/>\r\npages = {233\u2013246},<br \/>\r\npublisher = {Springer},<br \/>\r\naddress = {Jeju, South Korea},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('79','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_79\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-319-57529-2_19\" title=\"https:\/\/doi.org\/10.1007\/978-3-319-57529-2_19\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-319-57529-2_19<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-319-57529-2_19\" title=\"Follow DOI:10.1007\/978-3-319-57529-2_19\" target=\"_blank\">doi:10.1007\/978-3-319-57529-2_19<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('79','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2016\">2016<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yasuko Matsubara; Yasushi Sakurai<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('78','tp_links')\" style=\"cursor:pointer;\">Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), <\/span><span class=\"tp_pub_additional_pages\">pp. 1045\u20131054, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_address\">San Francisco, CA, <\/span><span class=\"tp_pub_additional_year\">2016<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_78\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('78','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_78\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{B14_2016_RegimeShiftsinStreams,<br \/>\r\ntitle = {Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences},<br \/>\r\nauthor = {Yasuko Matsubara and Yasushi Sakurai},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/2939672.2939755},<br \/>\r\ndoi = {10.1145\/2939672.2939755},<br \/>\r\nyear  = {2016},<br \/>\r\ndate = {2016-01-01},<br \/>\r\nbooktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016)},<br \/>\r\npages = {1045\u20131054},<br \/>\r\npublisher = {ACM},<br \/>\r\naddress = {San Francisco, CA},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('78','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_78\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/2939672.2939755\" title=\"https:\/\/doi.org\/10.1145\/2939672.2939755\" target=\"_blank\">https:\/\/doi.org\/10.1145\/2939672.2939755<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/2939672.2939755\" title=\"Follow DOI:10.1145\/2939672.2939755\" target=\"_blank\">doi:10.1145\/2939672.2939755<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('78','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-64","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/64","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/comments?post=64"}],"version-history":[{"count":11,"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/64\/revisions"}],"predecessor-version":[{"id":80,"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/pages\/64\/revisions\/80"}],"wp:attachment":[{"href":"https:\/\/www.db.sanken.osaka-u.ac.jp\/index.php\/wp-json\/wp\/v2\/media?parent=64"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}