{"id":230,"date":"2024-06-06T10:56:01","date_gmt":"2024-06-06T01:56:01","guid":{"rendered":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/?page_id=230"},"modified":"2024-12-20T14:26:12","modified_gmt":"2024-12-20T05:26:12","slug":"outcomes","status":"publish","type":"page","link":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/research\/outcomes.html","title":{"rendered":"\u5b66\u751f\u306e\u7814\u7a76\u6210\u679c"},"content":{"rendered":"<h3>\u8ad6\u6587\u8a8c<\/h3>\n<ul>\n<li>Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization. <strong>Transactions of the Japanese Society for Artificial Intelligence<\/strong>, 26(6), pp. C-L44_1-12, 2021.<\/li>\n<li>Kentaro Kanamori, Hiroki Arimura: Fairness-Aware Decision Tree Editing Based on Mixed-Integer Linear Optimization.\u00a0<strong>Transactions of the Japanese Society for Artificial Intelligence<\/strong>, 36(4), pp. B-L13_1-10, 2021.<\/li>\n<\/ul>\n<h3>\u67fb\u8aad\u4ed8\u304d\u56fd\u969b\u4f1a\u8b70<\/h3>\n<ul>\n<li>Yuto Shida, Giulia Punzi, Yasuaki Kobayashi, Takeaki Uno, Hiroki Arimura: Finding Diverse Strings and Longest Common Subsequences in a Graph. <strong>CPM 2024<\/strong>, LIPIcs vol. 296, pp. 27:1-27:19, 2024.<\/li>\n<li>Hiroki Arimura, Shunsuke Inenaga, Yasuaki Kobayashi, Yuto Nakashima, Mizuki Sue: Optimally Computing Compressed Indexing Arrays Based on the Compact Directed Acyclic Word Graph. <strong>SPIRE 2023<\/strong>, LNCS vol. 12240, pp. 28-34, 2023.<\/li>\n<li>Tsubasa Oizumi, Takeshi Kai, Takuya Mieno, Shunsuke Inenaga, Hiroki Arimura: Cartesian Tree Subsequence Matching. <strong>CPM 2022<\/strong>, LIPIcs vol. 223, 14:1-14:18, 2022.<\/li>\n<li>Kota Mata, Kentaro Kanamori, Hiroki Arimura: Computing the Collection of Good Models for Rule Lists. <strong>MLDM 2022<\/strong>, 151-165, 2022.<\/li>\n<li>Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura: Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. <strong>AAAI 2021<\/strong>, pp. 11564-11574, 2021.<\/li>\n<li>Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura: DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization. <strong>IJCAI 2020<\/strong>, pp. 2855-2862, 2020.<\/li>\n<\/ul>\n<h3>\u56fd\u969b\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7<\/h3>\n<ul>\n<li>Ken Nishida, Kojiro Machi, Kazuma Onishi, Katsuhiko Hayashi, Hidetaka Kamigaito: Multi-label Learning with Random Circular Vectors. <strong>RepL4NLP-2024<\/strong>, ACL 2024 workshop, Bangkok, 2024.<\/li>\n<li><span class=\"speaker\">Yuta Tsuruzono<\/span>, Hiroki Arimura and Shunsuke Inenaga: Enumerating Maximal Repeats in a Text Based on Bidirectional Indices. <strong>WAAC 2024<\/strong>, Seoul, 2024.<\/li>\n<li>Hiroki Arimura, Tatsuya Gima, Yasuaki Kobayashi, Hiroomi Nochide, Yota Otachi: Minimum Consistent Subset for Trees Revisited. <strong>WAAC 2023<\/strong>, Nagoya, 2023.<\/li>\n<li>Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata, Hiroki Arimura: Enumeration of Distinct Support Vectors for Interactive Decision Making. <strong>HILL 2019<\/strong>, California, 2019.<\/li>\n<\/ul>\n<h3>\u56fd\u5185\u7814\u7a76\u4f1a\u30fb\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7<\/h3>\n<ul>\n<li>\u897f\u7530 \u62f3\uff0c\u6797 \u514b\u5f66\uff0c\u4e0a\u57a3\u5916 \u82f1\u525b\uff0c\u9032\u85e4 \u88d5\u4e4b\uff1aLearned Bloom Filter 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\u7b2c126\u56de\u4eba\u5de5\u77e5\u80fd\u57fa\u672c\u554f\u984c\u7814\u7a76\u4f1a\uff0c2023\uff0e<\/li>\n<li>\u821f\u5c71\u8ad2\uff0c\u5c0f\u6797\u9756\u660e\uff1a\u591a\u69d8\u306a\u6700\u77ed\u7d4c\u8def\u3092\u6c42\u3081\u308b\u56fa\u5b9a\u30d1\u30e9\u30e1\u30fc\u30bf\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff0e2023\u5e74\u5ea6\u590f\u306eLA\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff0c2023\uff0e<\/li>\n<li>\u821f\u5c71\u8ad2\uff1a\u7d44\u5408\u305b\u6700\u9069\u5316\u554f\u984c\u306b\u304a\u3051\u308b\u591a\u69d8\u306a\u89e3\u306e\u767a\u898b\uff0e\u30d5\u30a9\u30ec\u30b9\u30c8\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d72023\uff0c2023\uff0e<\/li>\n<li>\u5f8c\u51fa\u7965\u81e3\uff1a\u6728\u306b\u304a\u3051\u308b\u6700\u5c0f\u7121\u77db\u76fe\u96c6\u5408\u3092\u6c42\u3081\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff0e\u30d5\u30a9\u30ec\u30b9\u30c8\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d72023\uff0c2023\uff0e<\/li>\n<li>\u9808\u6c5f\u745e\u6a39\uff0c\u5c0f\u6797\u9756\u660e\uff0c\u6709\u6751\u535a\u7d00\uff0c\u4e2d\u5cf6\u7950\u4eba\uff0c\u7a32\u6c38\u4fca\u4ecb\uff1a\u30b3\u30f3\u30d1\u30af\u30c8\u975e\u5de1\u56de\u30b0\u30e9\u30d5\u306b\u57fa\u3065\u304f\u9023\u9577\u5727\u7e2eBurrows-Wheeler\u5909\u63db\u306e\u52b9\u7387\u826f\u3044\u69cb\u7bc9\uff0e\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a \u30b3\u30f3\u30d4\u30e5\u30c6\u30fc\u30b7\u30e7\u30f3\u7814\u7a76\u4f1a\uff0c2022\uff0e<\/li>\n<li>\u5bae\ufa11\u73b2\u5b50\uff0c\u6709\u6751\u535a\u7d00\uff0c\u5c0f\u6797\u9756\u660e\uff1a\u534a\u9806\u5e8f\u96c6\u5408\u306e\u5f31\u57cb\u8fbc\u554f\u984c\u306b\u5bfe\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\uff0e\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a \u30b3\u30f3\u30d4\u30e5\u30c6\u30fc\u30b7\u30e7\u30f3\u7814\u7a76\u4f1a\uff0c2022\uff0e<\/li>\n<li>\u767d\u4e95\u6e09\u592a\uff0c\u6709\u6751\u535a\u7d00\uff1a\u5c0f\u3055\u304f\u3066\u9ad8\u7cbe\u5ea6\u306a\u6c7a\u5b9a\u6728\u96c6\u5408\u306b\u5bfe\u3059\u308b\u591a\u6b21\u5143\u5206\u5272\u8868\u306e\u52b9\u7387\u826f\u3044\u8a08\u7b97\uff0e\u7b2c25\u56de\u60c5\u5831\u8ad6\u7684\u5b66\u7fd2\u7406\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7 (IBIS2022)\uff0c2022\uff0e<\/li>\n<li>\u53c8\u5eb7\u592a\uff0c\u91d1\u68ee\u61b2\u592a\u6717\uff0c\u6709\u6751\u535a\u7d00\uff1a\u30eb\u30fc\u30eb\u30ea\u30b9\u30c8\u306b\u5bfe\u3059\u308bRashomon\u96c6\u5408\u306e\u53b3\u5bc6\u8a08\u7b97\u3068\u4e88\u6e2c\u591a\u91cd\u6027\u89e3\u6790\uff0e\u7b2c24\u56de\u60c5\u5831\u8ad6\u7684\u5b66\u7fd2\u7406\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7 (IBIS2021), 2021.<\/li>\n<\/ul>\n<h3>\u305d\u306e\u4ed6<\/h3>\n<ul>\n<li>Ryo Funayama, Yasuaki Kobayashi, Takeaki Uno: Parameterized Complexity of Finding Dissimilar Shortest Paths. arXiv:2402.14376, 2024.<\/li>\n<li>Hiroki Arimura, Tatsuya Gima, Yasuaki Kobayashi, Hiroomi Nochide, Yota Otachi: Minimum Consistent Subset for Trees Revisited. arXiv:2305.07259, 2023.<\/li>\n<li>Kota Mata, Kentaro Kanamori, Hiroki Arimura: Computing the Collection of Good Models for Rule Lists. arXiv:2204.11285, 2022.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u8ad6\u6587\u8a8c Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura: Distribution-Aware Counterfactual Explanat [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":28,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/pages\/230"}],"collection":[{"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/comments?post=230"}],"version-history":[{"count":15,"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/pages\/230\/revisions"}],"predecessor-version":[{"id":315,"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/pages\/230\/revisions\/315"}],"up":[{"embeddable":true,"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/pages\/28"}],"wp:attachment":[{"href":"https:\/\/c-mng.cwh.hokudai.ac.jp\/www-ikn.ist\/Root\/wp-json\/wp\/v2\/media?parent=230"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}