Non-Stationary Representation Learning in Sequential Linear Bandits-Reference-Cited by-同舟云学术

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Non-Stationary Representation Learning in Sequential Linear Bandits

Published:2022 Issue: Volume:1 Page:41-56
ISSN:2694-085X
Container-title:IEEE Open Journal of Control Systems
language:
Short-container-title:IEEE Open J. Control. Syst.

Author:

Qin Yuzhen¹^ORCID,Menara Tommaso²^ORCID,Oymak Samet³^ORCID,Ching ShiNung⁴^ORCID,Pasqualetti Fabio¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of California, Riverside, CA, USA

2. Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla CA, USA

3. Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA

4. Department of Electrical and Systems Engineering and Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA

Funder

ARO

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9552933/9683993/09783063.pdf?arnumber=9783063

Reference46 articles.

1. Linearly Parameterized Bandits

2. The Rotation of Eigenvectors by a Perturbation. III

3. Multi-task learning for contextual bandits;deshmukh;Proc Adv Neural Inf Process Syst,0

4. Multi-task linear bandits;soare;Proc Workshop Transfer Multi-task Learn Theory Meets Pract,0

5. Meta-learning with stochastic linear bandits;cella;Proc Int Conf Mach Learn,0

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret;IEEE Open Journal of Control Systems;2024

2. Hierarchical Reinforcement Learning for Non-Stationary Environments;2023 IEEE Symposium Series on Computational Intelligence (SSCI);2023-12-05

3. Cooperative Learning for Adversarial Multi-Armed Bandit on Open Multi-Agent Systems;IEEE Control Systems Letters;2023

4. Stochastic Multi-Armed Bandits with Non-Stationary Rewards Generated by a Linear Dynamical System;2022 IEEE 61st Conference on Decision and Control (CDC);2022-12-06

5. Representation Learning for Context-Dependent Decision-Making;2022 American Control Conference (ACC);2022-06-08

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