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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Main Authors: Cheung, Wang Chi, Simchi-Levi, David, Zhu, Ruihao
Other Authors: Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Format: Article
Language:English
Published: Elsevier BV 2021
Online Access:https://hdl.handle.net/1721.1/137064
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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.
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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
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