Learning to Optimize Under Non-Stationarity
© 2019 by the author(s). We introduce algorithms that achieve state-of-the-art dynamic regret bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment. We show how the difficulty posed by the non...
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| Andre forfattere: | |
| Format: | Article |
| Sprog: | English |
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Elsevier BV
2021
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| Online adgang: | https://hdl.handle.net/1721.1/137064 |
| _version_ | 1826190567853260800 |
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| author | Cheung, Wang Chi Simchi-Levi, David Zhu, Ruihao |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Cheung, Wang Chi Simchi-Levi, David Zhu, Ruihao |
| author_sort | Cheung, Wang Chi |
| collection | MIT |
| description | © 2019 by the author(s). We introduce algorithms that achieve state-of-the-art dynamic regret bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment. We show how the difficulty posed by the non-stationarity can be overcome by a novel marriage between stochastic and adversarial bandits learning algorithms. Defining d, BT, and T as the problem dimension, the variation budget, and the total time horizon, respectively, our main contributions are the tuned Sliding Window UCB (SW-UCB) algorithm with optimal Oe(d2/3(BT + 1)1/3T2/3) dynamic regret, and the tuning free bandit-over-bandit (BOB) framework built on top of the SW-UCB algorithm with best Oe(d2/3(BT + 1)1/4T3/4) dynamic regret. |
| first_indexed | 2024-09-23T08:41:58Z |
| format | Article |
| id | mit-1721.1/137064 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:41:58Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1370642023-01-30T21:21:03Z Learning to Optimize Under Non-Stationarity Cheung, Wang Chi Simchi-Levi, David Zhu, Ruihao Massachusetts Institute of Technology. Institute for Data, Systems, and Society Statistics and Data Science Center (Massachusetts Institute of Technology) © 2019 by the author(s). We introduce algorithms that achieve state-of-the-art dynamic regret bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment. We show how the difficulty posed by the non-stationarity can be overcome by a novel marriage between stochastic and adversarial bandits learning algorithms. Defining d, BT, and T as the problem dimension, the variation budget, and the total time horizon, respectively, our main contributions are the tuned Sliding Window UCB (SW-UCB) algorithm with optimal Oe(d2/3(BT + 1)1/3T2/3) dynamic regret, and the tuning free bandit-over-bandit (BOB) framework built on top of the SW-UCB algorithm with best Oe(d2/3(BT + 1)1/4T3/4) dynamic regret. 2021-11-02T12:19:41Z 2021-11-02T12:19:41Z 2018 2020-06-02T16:54:50Z Article http://purl.org/eprint/type/ConferencePaper 1556-5068 https://hdl.handle.net/1721.1/137064 Cheung, Wang Chi, Simchi-Levi, David and Zhu, Ruihao. 2018. "Learning to Optimize Under Non-Stationarity." AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 89. en 10.2139/ssrn.3261050 AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Elsevier BV arXiv |
| spellingShingle | Cheung, Wang Chi Simchi-Levi, David Zhu, Ruihao Learning to Optimize Under Non-Stationarity |
| title | Learning to Optimize Under Non-Stationarity |
| title_full | Learning to Optimize Under Non-Stationarity |
| title_fullStr | Learning to Optimize Under Non-Stationarity |
| title_full_unstemmed | Learning to Optimize Under Non-Stationarity |
| title_short | Learning to Optimize Under Non-Stationarity |
| title_sort | learning to optimize under non stationarity |
| url | https://hdl.handle.net/1721.1/137064 |
| work_keys_str_mv | AT cheungwangchi learningtooptimizeundernonstationarity AT simchilevidavid learningtooptimizeundernonstationarity AT zhuruihao learningtooptimizeundernonstationarity |