{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:55:28Z","timestamp":1773482128474,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T00:00:00Z","timestamp":1717891200000},"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":[[2024,6,14]]},"DOI":"10.1145\/3663742.3663974","type":"proceedings-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T11:45:08Z","timestamp":1715946308000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Low Rank Approximation for Learned Query Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8015-8486","authenticated-orcid":false,"given":"Zixuan","family":"Yi","sequence":"first","affiliation":[{"name":"University of Pennsylvania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6876-5059","authenticated-orcid":false,"given":"Yao","family":"Tian","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7527-2957","authenticated-orcid":false,"given":"Zachary G.","family":"Ives","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-1124","authenticated-orcid":false,"given":"Ryan","family":"Marcus","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}]}],"member":"320","published-online":{"date-parts":[[2024,6,9]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. PostgreSQL Database http:\/\/www.postgresql.org\/. ([n. d.])."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611544"},{"key":"e_1_3_2_1_3_1","volume-title":"Candes and Terence Tao","author":"Emmanuel","year":"2009","unstructured":"Emmanuel J. Candes and Terence Tao. 2009. The Power of Convex Relaxation: Near-Optimal Matrix Completion. http:\/\/arxiv.org\/abs\/0903.1476arXiv:0903.1476 [cs, math]."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-009-9045-5"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3587136.3587150"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132772"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/138859.138867"},{"key":"e_1_3_2_1_8_1","unstructured":"Trevor Hastie Rahul Mazumder Jason Lee and Reza Zadeh. 2014. Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares. arXiv:1410.2596 [stat.ME]"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2401.15210"},{"key":"e_1_3_2_1_10_1","volume-title":"Adam: A Method for Stochastic Optimization. In 3rd International Conference for Learning Representations (ICLR '15)","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference for Learning Representations (ICLR '15). San Diego, CA."},{"key":"e_1_3_2_1_11_1","volume-title":"Learning to Optimize Join Queries With Deep Reinforcement Learning. arXiv:1808.03196 [cs] (Aug","author":"Krishnan Sanjay","year":"2018","unstructured":"Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph Hellerstein, and Ion Stoica. 2018. Learning to Optimize Join Queries With Deep Reinforcement Learning. arXiv:1808.03196 [cs] (Aug. 2018). arXiv:1808.03196 [cs]"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452838"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342644"},{"key":"e_1_3_2_1_15_1","volume-title":"Deep Reinforcement Learning for Join Order Enumeration. In First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM @ SIGMOD '18)","author":"Marcus Ryan","year":"2018","unstructured":"Ryan Marcus and Olga Papaemmanouil. 2018. Deep Reinforcement Learning for Join Order Enumeration. In First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM @ SIGMOD '18). Houston, TX."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10139"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476259"},{"key":"e_1_3_2_1_18_1","volume-title":"Learning State Representations for Query Optimization with Deep Reinforcement Learning. In 2nd Workshop on Data Managmeent for End-to-End Machine Learning (DEEM '18)","author":"Ortiz Jennifer","unstructured":"Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, and S. Sathiya Keerthi. 2018. Learning State Representations for Query Optimization with Deep Reinforcement Learning. In 2nd Workshop on Data Managmeent for End-to-End Machine Learning (DEEM '18)."},{"key":"e_1_3_2_1_19_1","volume-title":"Automatic Differentiation in PyTorch. In Neural Information Processing Workshops (NIPS-W '17)","author":"Paszke Adam","year":"2017","unstructured":"Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic Differentiation in PyTorch. In Neural Information Processing Workshops (NIPS-W '17)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-934613-53-8.50038-8"},{"key":"e_1_3_2_1_21_1","volume-title":"Advances in Neural Information Processing Systems","author":"Srebro Nathan","year":"2004","unstructured":"Nathan Srebro, Jason Rennie, and Tommi Jaakkola. 2004. Maximum-Margin Matrix Factorization. In Advances in Neural Information Processing Systems, Vol. 17. MIT Press. https:\/\/papers.nips.cc\/paper_files\/paper\/2004\/hash\/e0688d13958a19e087e123148555e4b4-Abstract.html"},{"key":"e_1_3_2_1_22_1","first-page":"1","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research 15, 1 (Jan. 2014), 1929--1958.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_1_23_1","volume-title":"VLDB (VLDB '01)","author":"Stillger Michael","year":"2001","unstructured":"Michael Stillger, Guy M. Lohman, Volker Markl, and Mokhtar Kandil. 2001. LEO - DB2's LEarning Optimizer. In VLDB (VLDB '01). 19--28."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00294"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641205"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611479.3611528"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517885"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3565838.3565846"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00116"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526052"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583160"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641209"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626246.3653391"}],"event":{"name":"SIGMOD\/PODS '24: International Conference on Management of Data","location":"Santiago AA Chile","acronym":"SIGMOD\/PODS '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663742.3663974","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3663742.3663974","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T20:33:50Z","timestamp":1755981230000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3663742.3663974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,9]]},"references-count":33,"alternative-id":["10.1145\/3663742.3663974","10.1145\/3663742"],"URL":"https:\/\/doi.org\/10.1145\/3663742.3663974","relation":{},"subject":[],"published":{"date-parts":[[2024,6,9]]},"assertion":[{"value":"2024-06-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}