{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:13Z","timestamp":1750309333885,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671827","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"3758-3769","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Decision Rule List Learning via Unified Sequence Submodular Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9558-7163","authenticated-orcid":false,"given":"Linxiao","family":"Yang","sequence":"first","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5231-5452","authenticated-orcid":false,"given":"Jingbang","family":"Yang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5835-7259","authenticated-orcid":false,"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning optimal classification trees: Strong max-flow formulations. arXiv preprint arXiv:2002.09142","author":"Aghaei Sina","year":"2020","unstructured":"Sina Aghaei, Andres Gomez, and Phebe Vayanos. 2020. Learning optimal classification trees: Strong max-flow formulations. arXiv preprint arXiv:2002.09142 (2020)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5711"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2020.3820"},{"key":"e_1_3_2_2_4_1","volume-title":"Maximizing sequence-submodular functions. arXiv preprint arXiv:1009.4153","author":"Alaei Saeed","year":"2010","unstructured":"Saeed Alaei and Azarakhsh Malekian. 2010. Maximizing sequence-submodular functions. arXiv preprint arXiv:1009.4153 (2010)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098047"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.2196\/15154"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1144"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1711236115"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/icaps.v30i1.6643"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103486"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5633-9"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973402.106"},{"key":"e_1_3_2_2_13_1","unstructured":"Allison Chang Dimitris Bertsimas and Cynthia Rudin. 2012. An Integer Optimization Approach to Associative Classification. In Advances in Neural Information Processing Systems. 269--277."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3091622.3091637"},{"key":"e_1_3_2_2_15_1","unstructured":"Sanjeeb Dash Oktay Gunluk and Dennis Wei. 2018. Boolean Decision Rules via Column Generation. In Advances in Neural Information Processing Systems. 4655--4665."},{"key":"e_1_3_2_2_16_1","volume-title":"Empirical analysis of the commercial loan classification decision. Accounting Review","author":"Richard Dietrich J","year":"1982","unstructured":"J Richard Dietrich and Robert S Kaplan. 1982. Empirical analysis of the commercial loan classification decision. Accounting Review (1982), 18--38."},{"key":"e_1_3_2_2_17_1","volume-title":"The accuracy, fairness, and limits of predicting recidivism. Science advances","author":"Dressel Julia","year":"2018","unstructured":"Julia Dressel and Hany Farid. 2018. The accuracy, fairness, and limits of predicting recidivism. Science advances, Vol. 4, 1 (2018), eaao5580."},{"key":"e_1_3_2_2_18_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_2_19_1","unstructured":"Jonathan Eckstein Noam Goldberg and Ai Kagawa. 2017. Rule-enhanced penalized regression by column generation using rectangular maximum agreement. In ICML. PMLR 1059--1067."},{"key":"e_1_3_2_2_20_1","volume-title":"Interpretability in healthcare: A comparative study of local machine learning interpretability techniques. Computational Intelligence","author":"ElShawi Radwa","year":"2020","unstructured":"Radwa ElShawi, Youssef Sherif, Mouaz Al-Mallah, and Sherif Sakr. 2020. Interpretability in healthcare: A comparative study of local machine learning interpretability techniques. Computational Intelligence (2020)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1137\/090779346"},{"key":"e_1_3_2_2_22_1","volume-title":"MIT, University of Oxford, UC Irvine, and UC Berkeley.","author":"Google FICO","year":"2018","unstructured":"FICO, Google, Imperial College London, MIT, University of Oxford, UC Irvine, and UC Berkeley. 2018. Explainable Machine Learning Challenge. https:\/\/community.fico.com\/s\/explainable-machine-learning-challenge"},{"key":"e_1_3_2_2_23_1","volume-title":"Meel","author":"Ghosh Bishwamittra","year":"2020","unstructured":"Bishwamittra Ghosh, Dmitry Malioutov, and Kuldeep S. Meel. 2020. Classification Rules in Relaxed Logical Form. In Proc. of ECAI."},{"key":"e_1_3_2_2_24_1","volume-title":"Meel","author":"Ghosh Bishwamittra","year":"2019","unstructured":"Bishwamittra Ghosh and Kuldeep S. Meel. 2019. IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules. In Proc. of AIES."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"key":"e_1_3_2_2_26_1","unstructured":"Chris Harshaw Moran Feldman Justin Ward and Amin Karbasi. 2019. Submodular maximization beyond non-negativity: Guarantees fast algorithms and applications. In ICML. PMLR 2634--2643."},{"key":"e_1_3_2_2_27_1","unstructured":"Rishabh Iyer Stefanie Jegelka and Jeff Bilmes. 2013. Fast semidifferential-based submodular function optimization. In ICML. PMLR 855--863."},{"key":"e_1_3_2_2_28_1","volume-title":"Ordered submodularity and its applications to diversifying recommendations. arXiv preprint arXiv:2203.00233","author":"Kleinberg Jon","year":"2022","unstructured":"Jon Kleinberg, Emily Ryu, and \u00c9va Tardos. 2022. Ordered submodularity and its applications to diversifying recommendations. arXiv preprint arXiv:2203.00233 (2022)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139177801.004"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939874"},{"key":"e_1_3_2_2_31_1","unstructured":"Himabindu Lakkaraju and Cynthia Rudin. 2017. Learning cost-effective and interpretable treatment regimes. In Artificial intelligence and statistics. PMLR 166--175."},{"key":"e_1_3_2_2_32_1","volume-title":"How We Analyzed the COMPAS Recidivism Algorithm. ProPublica","author":"Larson Jeff","year":"2016","unstructured":"Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin. 2016. How We Analyzed the COMPAS Recidivism Algorithm. ProPublica (2016)."},{"key":"e_1_3_2_2_33_1","unstructured":"Jimmy Lin Chudi Zhong Diane Hu Cynthia Rudin and Margo Seltzer. 2020. Generalized and scalable optimal sparse decision trees. In ICML. PMLR 6150--6160."},{"key":"e_1_3_2_2_34_1","first-page":"18","article-title":"Explainable AI","volume":"23","author":"Linardatos Pantelis","year":"2021","unstructured":"Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2021. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, Vol. 23, 1 (2021), 18.","journal-title":"A Review of Machine Learning Interpretability Methods. Entropy"},{"volume-title":"Proceedings of International Conference on Constraint Programming (CP).","author":"Malioutov Dmitry","key":"e_1_3_2_2_35_1","unstructured":"Dmitry Malioutov and Kuldeep S. Meel. 2018. MLIC: A MaxSAT-Based framework for learning interpretable classification rules. In Proceedings of International Conference on Constraint Programming (CP)."},{"key":"e_1_3_2_2_36_1","volume-title":"LIBRE: Learning interpretable boolean rule ensembles. In ICAIS. PMLR, 245--255.","author":"Mita Graziano","year":"2020","unstructured":"Graziano Mita, Paolo Papotti, Maurizio Filippone, and Pietro Michiardi. 2020. LIBRE: Learning interpretable boolean rule ensembles. In ICAIS. PMLR, 245--255."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00389"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Nina Narodytska Alexey Ignatiev Filipe Pereira Joao Marques-Silva and IS RAS. 2018. Learning Optimal Decision Trees with SAT.. In Ijcai. 1362--1368.","DOI":"10.24963\/ijcai.2018\/189"},{"key":"e_1_3_2_2_39_1","volume-title":"An analysis of approximations for maximizing submodular set functions-I. Mathematical programming","author":"Nemhauser George L","year":"1978","unstructured":"George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher. 1978. An analysis of approximations for maximizing submodular set functions-I. Mathematical programming, Vol. 14 (1978), 265--294."},{"key":"e_1_3_2_2_40_1","volume-title":"On the approximation relationship between optimizing ratio of submodular (rs) and difference of submodular (ds) functions. arXiv preprint arXiv:2101.01631","author":"Perrault Pierre","year":"2021","unstructured":"Pierre Perrault, Jennifer Healey, Zheng Wen, and Michal Valko. 2021. On the approximation relationship between optimizing ratio of submodular (rs) and difference of submodular (ds) functions. arXiv preprint arXiv:2101.01631 (2021)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.10.050"},{"key":"e_1_3_2_2_42_1","volume-title":"FOIL: A midterm report","author":"Ross Quinlan J","year":"1993","unstructured":"J Ross Quinlan and R Mike Cameron-Jones. 1993. FOIL: A midterm report. In ECML. Springer, 1--20."},{"key":"e_1_3_2_2_43_1","volume-title":"Learning decision lists. Machine learning","author":"Rivest Ronald L","year":"1987","unstructured":"Ronald L Rivest. 1987. Learning decision lists. Machine learning, Vol. 2, 3 (1987), 229--246."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova and Chudi Zhong. 2021. Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. arxiv: 2103.11251 [cs.LG]","DOI":"10.1214\/21-SS133"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12532-018-0143-8"},{"key":"e_1_3_2_2_46_1","volume-title":"NeurIPS","volume":"21","author":"Streeter Matthew","year":"2008","unstructured":"Matthew Streeter and Daniel Golovin. 2008. An online algorithm for maximizing submodular functions. NeurIPS, Vol. 21 (2008)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2601299"},{"key":"e_1_3_2_2_48_1","volume-title":"Proceedings of Artificial Intelligence and Statistics (AISTATS).","author":"Wang Fulton","year":"2015","unstructured":"Fulton Wang and Cynthia Rudin. 2015. Falling Rule Lists. In Proceedings of Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_2_49_1","unstructured":"Tong Wang and Cynthia Rudin. 2015. Learning Optimized Or's of And's. arxiv: 1511.02210 [cs.AI]"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.5555\/3122009.3176814"},{"key":"e_1_3_2_2_51_1","volume-title":"NeurIPS","volume":"34","author":"Yang Fan","year":"2021","unstructured":"Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, and Liang Sun. 2021. Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach. NeurIPS, Vol. 34 (2021)."},{"key":"e_1_3_2_2_52_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning. 3921--3930","author":"Yang Hongyu","year":"2017","unstructured":"Hongyu Yang, Cynthia Rudin, and Margo Seltzer. 2017. Scalable Bayesian Rule Lists. In Proceedings of the 34th International Conference on Machine Learning. 3921--3930."},{"key":"e_1_3_2_2_53_1","volume-title":"Pierre Le Bodic, and Peter J Stuckey","author":"Yu Jinqiang","year":"2020","unstructured":"Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic, and Peter J Stuckey. 2020. Optimal decision lists using SAT. arXiv preprint arXiv:2010.09919 (2020)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12354"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2015.2440566"},{"key":"e_1_3_2_2_56_1","first-page":"1771","article-title":"A scalable MIP-based method for learning optimal multivariate decision trees","volume":"33","author":"Zhu Haoran","year":"2020","unstructured":"Haoran Zhu, Pavankumar Murali, Dzung Phan, Lam Nguyen, and Jayant Kalagnanam. 2020. A scalable MIP-based method for learning optimal multivariate decision trees. NeurlIPS, Vol. 33 (2020), 1771--1781.","journal-title":"NeurlIPS"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Barcelona Spain","acronym":"KDD '24"},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671827","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671827","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":56,"alternative-id":["10.1145\/3637528.3671827","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671827","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}