{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:29:01Z","timestamp":1778498941522,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"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":[[2020,10,19]]},"DOI":"10.1145\/3412815.3416885","type":"proceedings-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T23:37:54Z","timestamp":1602805074000},"page":"129-138","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Towards Practical Lipschitz Bandits"],"prefix":"10.1145","author":[{"given":"Tianyu","family":"Wang","sequence":"first","affiliation":[{"name":"Duke University, Durham, NC, USA"}]},{"given":"Weicheng","family":"Ye","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}]},{"given":"Dawei","family":"Geng","sequence":"additional","affiliation":[{"name":"Autodesk, Inc., San Francisco, CA, USA"}]},{"given":"Cynthia","family":"Rudin","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Advances in Neural Information Processing Systems 24. Curran Associates","author":"Yasin","unstructured":"Yasin Abbasi-yadkori, D\u00e1vid P\u00e1l , and Csaba Szepesv\u00e1ri . 2011. Improved Algorithms for Linear Stochastic Bandits . In Advances in Neural Information Processing Systems 24. Curran Associates , Inc ., 2312--2320. Yasin Abbasi-yadkori, D\u00e1vid P\u00e1l, and Csaba Szepesv\u00e1ri. 2011. Improved Algorithms for Linear Stochastic Bandits. In Advances in Neural Information Processing Systems 24. Curran Associates, Inc., 2312--2320."},{"key":"e_1_3_2_1_2_1","volume-title":"JMLR Workshop and Conference Proceedings","author":"Agrawal Shipra","year":"2012","unstructured":"Shipra Agrawal and Navin Goyal . 2012 . Analysis of Thompson Sampling for the Multi-armed Bandit Problem (Proceedings of Machine Learning Research, Vol. 23) . JMLR Workshop and Conference Proceedings , Edinburgh, Scotland, 39.1--39.26. Shipra Agrawal and Navin Goyal. 2012. Analysis of Thompson Sampling for the Multi-armed Bandit Problem (Proceedings of Machine Learning Research, Vol. 23). JMLR Workshop and Conference Proceedings, Edinburgh, Scotland, 39.1--39.26."},{"key":"e_1_3_2_1_3_1","volume-title":"Thompson Sampling for Contextual Bandits with Linear Payoffs (Proceedings of Machine Learning Research","author":"Agrawal Shipra","unstructured":"Shipra Agrawal and Navin Goyal . 2013. Thompson Sampling for Contextual Bandits with Linear Payoffs (Proceedings of Machine Learning Research , Vol. 28). PMLR, Atlanta, Georgia, USA, 127-- 135 . Shipra Agrawal and Navin Goyal. 2013. Thompson Sampling for Contextual Bandits with Linear Payoffs (Proceedings of Machine Learning Research, Vol. 28). PMLR, Atlanta, Georgia, USA, 127--135."},{"key":"e_1_3_2_1_4_1","first-page":"397","article-title":"Using confidence bounds for exploitation-exploration trade-offs","volume":"3","author":"Auer Peter","year":"2002","unstructured":"Peter Auer . 2002 . Using confidence bounds for exploitation-exploration trade-offs . Journal of Machine Learning Research , Vol. 3 , Nov (2002), 397 -- 422 . Peter Auer. 2002. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research , Vol. 3, Nov (2002), 397--422.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_5_1","volume-title":"Finite-time analysis of the multiarmed bandit problem. Machine learning","author":"Auer Peter","year":"2002","unstructured":"Peter Auer , Nicolo Cesa-Bianchi , and Paul Fischer . 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning , Vol. 47 , 2--3 ( 2002 ), 235--256. Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning , Vol. 47, 2--3 (2002), 235--256."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72927-3_33"},{"key":"e_1_3_2_1_7_1","volume-title":"Classification and regression trees","author":"Breiman Leo","unstructured":"Leo Breiman , Jerome Friedman , Charles J Stone , and Richard A Olshen . 1984. Classification and regression trees . CRC press . Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. 1984. Classification and regression trees .CRC press."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","DOI":"10.1561\/9781601986276","volume-title":"Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends\u00ae in Machine Learning","author":"Bubeck S\u00e9bastien","year":"2012","unstructured":"S\u00e9bastien Bubeck and Nicolo Cesa-Bianchi . 2012. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends\u00ae in Machine Learning ( 2012 ). S\u00e9bastien Bubeck and Nicolo Cesa-Bianchi. 2012. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends\u00ae in Machine Learning (2012)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2021053"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of International Conference on Machine Learning. 253--261","author":"Contal Emile","year":"2014","unstructured":"Emile Contal , Vianney Perchet , and Nicolas Vayatis . 2014 . Gaussian process optimization with mutual information . In Proceedings of International Conference on Machine Learning. 253--261 . Emile Contal, Vianney Perchet, and Nicolas Vayatis. 2014. Gaussian process optimization with mutual information. In Proceedings of International Conference on Machine Learning. 253--261."},{"key":"e_1_3_2_1_11_1","volume-title":"Annual Conference on Learning Theory. 355--366","author":"Dani Varsha","year":"2008","unstructured":"Varsha Dani , Thomas P Hayes , and Sham M Kakade . 2008 . Stochastic Linear Optimization under Bandit Feedback .. In Annual Conference on Learning Theory. 355--366 . Varsha Dani, Thomas P Hayes, and Sham M Kakade. 2008. Stochastic Linear Optimization under Bandit Feedback.. In Annual Conference on Learning Theory. 355--366."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of International Conference on Machine Learning .","author":"de Freitas Nando","year":"2012","unstructured":"Nando de Freitas , Alex Smola , and Masrour Zoghi . 2012 . Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations . In Proceedings of International Conference on Machine Learning . Nando de Freitas, Alex Smola, and Masrour Zoghi. 2012. Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. In Proceedings of International Conference on Machine Learning ."},{"key":"e_1_3_2_1_13_1","series-title":"Series B (Methodological)","volume-title":"Bandit processes and dynamic allocation indices. Journal of the Royal Statistical Society","author":"Gittins John C","year":"1979","unstructured":"John C Gittins . 1979. Bandit processes and dynamic allocation indices. Journal of the Royal Statistical Society . Series B (Methodological) ( 1979 ), 148--177. John C Gittins. 1979. Bandit processes and dynamic allocation indices. Journal of the Royal Statistical Society. Series B (Methodological) (1979), 148--177."},{"key":"e_1_3_2_1_14_1","volume-title":"Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations .","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015 . Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations . Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations ."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1374376.1374475"},{"key":"e_1_3_2_1_16_1","volume-title":"Asymptotically efficient adaptive allocation rules. Advances in applied mathematics","author":"Lai Tze Leung","year":"1985","unstructured":"Tze Leung Lai and Herbert Robbins . 1985. Asymptotically efficient adaptive allocation rules. Advances in applied mathematics , Vol. 6 , 1 ( 1985 ), 4--22. Tze Leung Lai and Herbert Robbins. 1985. Asymptotically efficient adaptive allocation rules. Advances in applied mathematics , Vol. 6, 1 (1985), 4--22."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772758"},{"key":"e_1_3_2_1_18_1","volume-title":"Hyperband: Bandit-based configuration evaluation for hyperparameter optimization. The Journal of Machine Learning Research","author":"Li Lisha","year":"2016","unstructured":"Lisha Li , Kevin Jamieson , Giulia DeSalvo , Afshin Rostamizadeh , and Ameet Talwalkar . 2016 . Hyperband: Bandit-based configuration evaluation for hyperparameter optimization. The Journal of Machine Learning Research (2016). Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2016. Hyperband: Bandit-based configuration evaluation for hyperparameter optimization. The Journal of Machine Learning Research (2016)."},{"key":"e_1_3_2_1_19_1","volume-title":"Annual Conference on Learning Theory. 975--999","author":"Magureanu Stefan","year":"2014","unstructured":"Stefan Magureanu , Richard Combes , and Alexandre Proutiere . 2014 . Lipschitz bandits: Regret lower bound and optimal algorithms . In Annual Conference on Learning Theory. 975--999 . Stefan Magureanu, Richard Combes, and Alexandre Proutiere. 2014. Lipschitz bandits: Regret lower bound and optimal algorithms. In Annual Conference on Learning Theory. 975--999."},{"key":"e_1_3_2_1_20_1","volume-title":"Bayesopt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits. The Journal of Machine Learning Research","author":"Martinez-Cantin Ruben","year":"2014","unstructured":"Ruben Martinez-Cantin . 2014 . Bayesopt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits. The Journal of Machine Learning Research (2014). Ruben Martinez-Cantin. 2014. Bayesopt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits. The Journal of Machine Learning Research (2014)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2019.8919864"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_23_1","volume-title":"A variant of Azuma's inequality for martingales with sub-Gaussian tails. arXiv preprint arXiv:1110.2392","author":"Shamir Ohad","year":"2011","unstructured":"Ohad Shamir . 2011. A variant of Azuma's inequality for martingales with sub-Gaussian tails. arXiv preprint arXiv:1110.2392 ( 2011 ). Ohad Shamir. 2011. A variant of Azuma's inequality for martingales with sub-Gaussian tails. arXiv preprint arXiv:1110.2392 (2011)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670330"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the 27th International Conference on Machine Learning. Omnipress","author":"Srinivas Niranjan","year":"2010","unstructured":"Niranjan Srinivas , Andreas Krause , Sham Kakade , and Matthias Seeger . 2010 . Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design . In Proceedings of the 27th International Conference on Machine Learning. Omnipress , Haifa, Israel, 1015--1022. Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger. 2010. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In Proceedings of the 27th International Conference on Machine Learning. Omnipress, Haifa, Israel, 1015--1022."},{"key":"e_1_3_2_1_26_1","volume-title":"Introduction to reinforcement learning","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto . 1998. Introduction to reinforcement learning . Vol. 135 . MIT press Cambridge . Richard S Sutton and Andrew G Barto. 1998. Introduction to reinforcement learning . Vol. 135. MIT press Cambridge."},{"key":"e_1_3_2_1_27_1","volume-title":"On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika","author":"Thompson William R","year":"1933","unstructured":"William R Thompson . 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika ( 1933 ). William R Thompson. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika (1933)."},{"key":"e_1_3_2_1_28_1","volume-title":"Incremental induction of decision trees. Machine learning","author":"Utgoff Paul E","year":"1989","unstructured":"Paul E Utgoff . 1989. Incremental induction of decision trees. Machine learning , Vol. 4 , 2 ( 1989 ), 161--186. Paul E Utgoff. 1989. Incremental induction of decision trees. Machine learning , Vol. 4, 2 (1989), 161--186."}],"event":{"name":"FODS '20: ACM-IMS Foundations of Data Science Conference","location":"Virtual Event USA","acronym":"FODS '20"},"container-title":["Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3412815.3416885","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3412815.3416885","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:02Z","timestamp":1750195502000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3412815.3416885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,18]]},"references-count":28,"alternative-id":["10.1145\/3412815.3416885","10.1145\/3412815"],"URL":"https:\/\/doi.org\/10.1145\/3412815.3416885","relation":{},"subject":[],"published":{"date-parts":[[2020,10,18]]},"assertion":[{"value":"2020-10-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}