Journal articles on the topic 'Bandit algorithm'
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Ciucanu, Radu, Pascal Lafourcade, Gael Marcadet, and Marta Soare. "SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits." Journal of Artificial Intelligence Research 73 (February 23, 2022): 737–65. http://dx.doi.org/10.1613/jair.1.13163.
Zhou, Huozhi, Lingda Wang, Lav Varshney, and Ee-Peng Lim. "A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6933–40. http://dx.doi.org/10.1609/aaai.v34i04.6176.
Azizi, Javad, Branislav Kveton, Mohammad Ghavamzadeh, and Sumeet Katariya. "Meta-Learning for Simple Regret Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 6709–17. http://dx.doi.org/10.1609/aaai.v37i6.25823.
Kuroki, Yuko, Liyuan Xu, Atsushi Miyauchi, Junya Honda, and Masashi Sugiyama. "Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback." Neural Computation 32, no. 9 (September 2020): 1733–73. http://dx.doi.org/10.1162/neco_a_01299.
Li, Youxuan. "Improvement of the recommendation system based on the multi-armed bandit algorithm." Applied and Computational Engineering 36, no. 1 (January 22, 2024): 237–41. http://dx.doi.org/10.54254/2755-2721/36/20230453.
Liu, Zizhuo. "Investigation of progress and application related to Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (January 22, 2024): 155–59. http://dx.doi.org/10.54254/2755-2721/37/20230496.
Agarwal, Mridul, Vaneet Aggarwal, Abhishek Kumar Umrawal, and Chris Quinn. "DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 6557–65. http://dx.doi.org/10.1609/aaai.v35i8.16812.
Xue, Bo, Ji Cheng, Fei Liu, Yimu Wang, and Qingfu Zhang. "Multiobjective Lipschitz Bandits under Lexicographic Ordering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16238–46. http://dx.doi.org/10.1609/aaai.v38i15.29558.
Sharaf, Amr, and Hal Daumé III. "Meta-Learning Effective Exploration Strategies for Contextual Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9541–48. http://dx.doi.org/10.1609/aaai.v35i11.17149.
Nobari, Sadegh. "DBA: Dynamic Multi-Armed Bandit Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9869–70. http://dx.doi.org/10.1609/aaai.v33i01.33019869.
Qu, Jiaming. "Survey of dynamic pricing based on Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (January 22, 2024): 160–65. http://dx.doi.org/10.54254/2755-2721/37/20230497.
Niño-Mora, José. "A Fast-Pivoting Algorithm for Whittle’s Restless Bandit Index." Mathematics 8, no. 12 (December 15, 2020): 2226. http://dx.doi.org/10.3390/math8122226.
Lamberton, Damien, and Gilles Pagès. "A penalized bandit algorithm." Electronic Journal of Probability 13 (2008): 341–73. http://dx.doi.org/10.1214/ejp.v13-489.
Cheung, Wang Chi, David Simchi-Levi, and Ruihao Zhu. "Hedging the Drift: Learning to Optimize Under Nonstationarity." Management Science 68, no. 3 (March 2022): 1696–713. http://dx.doi.org/10.1287/mnsc.2021.4024.
Chen, Panyangjie. "Investigation of selection and application of Multi-Armed Bandit algorithms in recommendation system." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 185–90. http://dx.doi.org/10.54254/2755-2721/34/20230323.
Fourati, Fares, Christopher John Quinn, Mohamed-Slim Alouini, and Vaneet Aggarwal. "Combinatorial Stochastic-Greedy Bandit." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12052–60. http://dx.doi.org/10.1609/aaai.v38i11.29093.
Oswal, Urvashi, Aniruddha Bhargava, and Robert Nowak. "Linear Bandits with Feature Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5331–38. http://dx.doi.org/10.1609/aaai.v34i04.5980.
Tang, Qiao, Hong Xie, Yunni Xia, Jia Lee, and Qingsheng Zhu. "Robust Contextual Bandits via Bootstrapping." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12182–89. http://dx.doi.org/10.1609/aaai.v35i13.17446.
Li, Wenjie, Qifan Song, Jean Honorio, and Guang Lin. "Federated X-armed Bandit." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (March 24, 2024): 13628–36. http://dx.doi.org/10.1609/aaai.v38i12.29267.
Wang, Liangxu. "Investigation of frontier Multi-Armed Bandit algorithms and applications." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 179–84. http://dx.doi.org/10.54254/2755-2721/34/20230322.
Du, Yihan, Siwei Wang, and Longbo Huang. "A One-Size-Fits-All Solution to Conservative Bandit Problems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7254–61. http://dx.doi.org/10.1609/aaai.v35i8.16891.
Esfandiari, Hossein, Amin Karbasi, Abbas Mehrabian, and Vahab Mirrokni. "Regret Bounds for Batched Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7340–48. http://dx.doi.org/10.1609/aaai.v35i8.16901.
Han, Qi, Li Zhu, and Fei Guo. "Forced Exploration in Bandit Problems." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12270–77. http://dx.doi.org/10.1609/aaai.v38i11.29117.
Chen, Xijin, Kim May Lee, Sofia S. Villar, and David S. Robertson. "Some performance considerations when using multi-armed bandit algorithms in the presence of missing data." PLOS ONE 17, no. 9 (September 12, 2022): e0274272. http://dx.doi.org/10.1371/journal.pone.0274272.
Ene, Alina, Huy L. Nguyen, and Adrian Vladu. "Projection-Free Bandit Optimization with Privacy Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7322–30. http://dx.doi.org/10.1609/aaai.v35i8.16899.
Chen, Tianfeng. "Empirical performances comparison for ETC algorithm." Applied and Computational Engineering 13, no. 1 (October 23, 2023): 29–36. http://dx.doi.org/10.54254/2755-2721/13/20230705.
Zhu, Zhaowei, Jingxuan Zhu, Ji Liu, and Yang Liu. "Federated Bandit." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 1 (February 18, 2021): 1–29. http://dx.doi.org/10.1145/3447380.
Rangi, Anshuka, Long Tran-Thanh, Haifeng Xu, and Massimo Franceschetti. "Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 8054–61. http://dx.doi.org/10.1609/aaai.v36i7.20777.
Amani, Sanae, and Christos Thrampoulidis. "Decentralized Multi-Agent Linear Bandits with Safety Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 6627–35. http://dx.doi.org/10.1609/aaai.v35i8.16820.
Huang, Wen, Lu Zhang, and Xintao Wu. "Achieving Counterfactual Fairness for Causal Bandit." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6952–59. http://dx.doi.org/10.1609/aaai.v36i6.20653.
Narita, Yusuke, Shota Yasui, and Kohei Yata. "Efficient Counterfactual Learning from Bandit Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4634–41. http://dx.doi.org/10.1609/aaai.v33i01.33014634.
Zhao, Shanshan, Wenhai Cui, Bei Jiang, Linglong Kong, and Xiaodong Yan. "Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (March 24, 2024): 21815–22. http://dx.doi.org/10.1609/aaai.v38i19.30182.
Tolpin, David, and Solomon Shimony. "MCTS Based on Simple Rerget." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 193–99. http://dx.doi.org/10.1609/socs.v3i1.18221.
Li, Litao. "Exploring Multi-Armed Bandit algorithms: Performance analysis in dynamic environments." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 252–59. http://dx.doi.org/10.54254/2755-2721/34/20230338.
Oh, Min-hwan, and Garud Iyengar. "Multinomial Logit Contextual Bandits: Provable Optimality and Practicality." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9205–13. http://dx.doi.org/10.1609/aaai.v35i10.17111.
Varatharajah, Yogatheesan, and Brent Berry. "A Contextual-Bandit-Based Approach for Informed Decision-Making in Clinical Trials." Life 12, no. 8 (August 21, 2022): 1277. http://dx.doi.org/10.3390/life12081277.
Шиян, Дмитрий Николаевич, and Dmitry Shiyan. "One-armed bandit problem and the mirror descent algorithm." Mathematical Game Theory and Applications 15, no. 3 (February 2, 2024): 88–106. http://dx.doi.org/10.17076/mgta_2023_3_75.
Yu, Junpu. "Thompson -Greedy Algorithm: An Improvement to the Regret of Thompson Sampling and -Greedy on Multi-Armed Bandit Problems." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 525–34. http://dx.doi.org/10.54254/2755-2721/8/20230264.
Garcelon, Evrard, Mohammad Ghavamzadeh, Alessandro Lazaric, and Matteo Pirotta. "Improved Algorithms for Conservative Exploration in Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3962–69. http://dx.doi.org/10.1609/aaai.v34i04.5812.
Kasy, Maximilian, and Anja Sautmann. "Adaptive Treatment Assignment in Experiments for Policy Choice." Econometrica 89, no. 1 (2021): 113–32. http://dx.doi.org/10.3982/ecta17527.
Ontanon, Santiago. "The Combinatorial Multi-Armed Bandit Problem and Its Application to Real-Time Strategy Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 1 (June 30, 2021): 58–64. http://dx.doi.org/10.1609/aiide.v9i1.12681.
Patil, Vishakha, Ganesh Ghalme, Vineet Nair, and Y. Narahari. "Achieving Fairness in the Stochastic Multi-Armed Bandit Problem." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5379–86. http://dx.doi.org/10.1609/aaai.v34i04.5986.
Wang, Zhenlin, and Jonathan Scarlett. "Max-Min Grouped Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8603–11. http://dx.doi.org/10.1609/aaai.v36i8.20838.
Sakakibara, Masaya, Akira Notsu, Seiki Ubukata, and Katsuhiro Honda. "Designation of Candidate Solutions in Differential Evolution Based on Bandit Algorithm and its Evaluation." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 4 (July 20, 2019): 758–66. http://dx.doi.org/10.20965/jaciii.2019.p0758.
Kim, Gi-Soo, Jane P. Kim, and Hyun-Joon Yang. "Robust Tests in Online Decision-Making." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10016–24. http://dx.doi.org/10.1609/aaai.v36i9.21240.
Mansour, Yishay, Aleksandrs Slivkins, and Vasilis Syrgkanis. "Bayesian Incentive-Compatible Bandit Exploration." Operations Research 68, no. 4 (July 2020): 1132–61. http://dx.doi.org/10.1287/opre.2019.1949.
Ding, Wenkui, Tao Qin, Xu-Dong Zhang, and Tie-Yan Liu. "Multi-Armed Bandit with Budget Constraint and Variable Costs." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 232–38. http://dx.doi.org/10.1609/aaai.v27i1.8637.
Liu, Yizhi. "An investigation of progress related to stochastic stationary bandit algorithms." Applied and Computational Engineering 34, no. 1 (January 22, 2024): 197–201. http://dx.doi.org/10.54254/2755-2721/34/20230326.
Kaibel, Chris, and Torsten Biemann. "Rethinking the Gold Standard With Multi-armed Bandits: Machine Learning Allocation Algorithms for Experiments." Organizational Research Methods 24, no. 1 (June 11, 2019): 78–103. http://dx.doi.org/10.1177/1094428119854153.
Lupu, Andrei, Audrey Durand, and Doina Precup. "Leveraging Observations in Bandits: Between Risks and Benefits." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6112–19. http://dx.doi.org/10.1609/aaai.v33i01.33016112.