{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:15:48Z","timestamp":1766268948481,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671619","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"5752-5761","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["PEMBOT: Pareto-Ensembled Multi-task Boosted Trees"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0554-4141","authenticated-orcid":false,"given":"Gokul","family":"Swamy","sequence":"first","affiliation":[{"name":"Amazon, Seattle, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9580-4604","authenticated-orcid":false,"given":"Anoop","family":"Saladi","sequence":"additional","affiliation":[{"name":"Amazon, Bengaluru, KA, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-5073","authenticated-orcid":false,"given":"Arunita","family":"Das","sequence":"additional","affiliation":[{"name":"Amazon, Bengaluru, KA, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4298-8822","authenticated-orcid":false,"given":"Shobhit","family":"Niranjan","sequence":"additional","affiliation":[{"name":"Amazon, Bengaluru, KA, India"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007327622663"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Deblina Bhattacharjee Tong Zhang Sabine S\u00fcsstrunk and Mathieu Salzmann. 2022. MulT: An End-to-End Multitask Learning Transformer. (2022). arXiv:2205.08303 [cs.CV]","DOI":"10.1109\/CVPR52688.2022.01172"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2051445"},{"key":"e_1_3_2_2_4_1","volume-title":"Multitask Learning: A Knowledge-Based Source of Inductive Bias.","author":"Caruana Richard","year":"1993","unstructured":"Richard Caruana. 1993. Multitask Learning: A Knowledge-Based Source of Inductive Bias. (1993), 41--48."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.007"},{"key":"e_1_3_2_2_7_1","unstructured":"Laming Chen Guoxin Zhang and Hanning Zhou. 2018. Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity. arXiv:1709.05135 [cs.IR]"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_9_1","first-page":"547","article-title":"Modeling wine preferences by data mining from physicochemical properties","volume":"47","author":"Cortez P","year":"2009","unstructured":"P Cortez, A Cerdeira, F Almeida, T Matos, and J Reis. 2009. Modeling wine preferences by data mining from physicochemical properties. Elsevier 47, 4 (2009), 547--553.","journal-title":"Elsevier"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3646-3"},{"key":"e_1_3_2_2_11_1","volume-title":"Multiple-Gradient Descent Algorithm (MGDA) for Pareto-Front Identification. 34","author":"D\u00e9sid\u00e9ri Jean-Antoine","year":"2014","unstructured":"Jean-Antoine D\u00e9sid\u00e9ri. 2014. Multiple-Gradient Descent Algorithm (MGDA) for Pareto-Front Identification. 34 (2014). https:\/\/hal.inria.fr\/hal-01096049"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-15892-1_11"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106059"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-007-9037-z"},{"key":"e_1_3_2_2_16_1","unstructured":"Yury Gorishniy Ivan Rubachev Valentin Khrulkov and Artem Babenko. 2021. Revisiting Deep Learning Models for Tabular Data. (2021)."},{"key":"e_1_3_2_2_17_1","volume-title":"Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track","author":"Grinsztajn Leo","year":"2022","unstructured":"Leo Grinsztajn, Edouard Oyallon, and Gael Varoquaux. 2022. Why do tree-based models still outperform deep learning on typical tabular data? Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)."},{"key":"e_1_3_2_2_18_1","unstructured":"Insu Han Prabhanjan Kambadur Kyoungsoo Park and Jinwoo Shin. 2017. Faster Greedy MAP Inference for Determinantal Point Processes. arXiv:1703.03389 [cs.DM]"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"J. Horn N. Nafpliotis and D. E. Goldberg. 1994. A niched Pareto genetic algorithm for multiobjective optimization. (1994) 82--87 vol.1.","DOI":"10.1109\/ICEC.1994.350037"},{"key":"e_1_3_2_2_20_1","unstructured":"Xin Huang Ashish Khetan Milan Cvitkovic and Zohar Karnin. 2020. Tab-Transformer: Tabular Data Modeling Using Contextual Embeddings. (2020). arXiv:2012.06678 [cs.LG]"},{"key":"e_1_3_2_2_21_1","volume-title":"GATE: Gated Additive Tree Ensemble for Tabular Classification and Regression.","author":"Joseph Manu","year":"2023","unstructured":"Manu Joseph and Harsh Raj. 2023. GATE: Gated Additive Tree Ensemble for Tabular Classification and Regression. (2023). arXiv:2207.08548 [cs.LG]"},{"key":"e_1_3_2_2_22_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017), 3146--3154."},{"key":"e_1_3_2_2_23_1","unstructured":"Alex Kendall Yarin Gal and Roberto Cipolla. 2017. Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. (2017). http: \/\/arxiv.org\/abs\/1705.07115 cite arxiv:1705.07115."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1980.1102314"},{"key":"e_1_3_2_2_25_1","unstructured":"Xi Lin Hui-Ling Zhen Zhenhua Li Qing-Fu Zhang and Sam Kwong. 2019. Pareto Multi-Task Learning. (2019) 12060--12070. http:\/\/papers.nips.cc\/paper\/9374-pareto-multi-task-learning.pdf"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2013.00021"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01588971"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64063-1_7"},{"key":"e_1_3_2_2_29_1","unstructured":"Ozan Sener and Vladlen Koltun. 2018. Multi-Task Learning as Multi-Objective Optimization. (2018) 527--538. http:\/\/papers.nips.cc\/paper\/7334-multi-task-learning-as-multi-objective-optimization.pdf"},{"key":"e_1_3_2_2_30_1","volume-title":"TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442","author":"Arik Sercan O","year":"2019","unstructured":"O Arik Sercan and Tomas Pfister. 2019. TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442 (2019)."},{"key":"e_1_3_2_2_31_1","volume-title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. arXiv preprint arXiv:2106.01342","author":"Somepalli Gowthami","year":"2021","unstructured":"Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein. 2021. SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. arXiv preprint arXiv:2106.01342 (2021)."},{"key":"e_1_3_2_2_32_1","volume-title":"Hospedales","author":"Yang Yongxin","year":"2016","unstructured":"Yongxin Yang and Timothy M. Hospedales. 2016. Trace Norm Regularised Deep Multi-Task Learning. CoRR abs\/1606.04038 (2016). arXiv:1606.04038 http: \/\/arxiv.org\/abs\/1606.04038"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2006.12.011"}],"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\/3637528.3671619","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:59Z","timestamp":1750291559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":33,"alternative-id":["10.1145\/3637528.3671619","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671619","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"}}]}}