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2007.01980
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Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
4 July 2020
Yufei Ruan
Jiaqi Yang
Yuanshuo Zhou
OffRL
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Papers citing
"Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design"
41 / 41 papers shown
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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
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Breaking the
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Batched Stochastic Bandit for Nondegenerate Functions
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Generalized Linear Bandits with Limited Adaptivity
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Batched Nonparametric Contextual Bandits
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Federated Linear Bandits with Finite Adversarial Actions
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Chao Tian
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373
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Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
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Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
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From Random Search to Bandit Learning in Metric Measure Spaces
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488
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Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
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Yifei Min
Jiafan He
Tianhao Wang
Quanquan Gu
397
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CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
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Joel Jennings
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Cheng Zhang
Adam Foster
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Active learning for data streams: a survey
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A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization
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Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
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Lin F. Yang
Christina Fragouli
380
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Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
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257
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Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
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335
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Contextual Bandits with Large Action Spaces: Made Practical
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Dylan J. Foster
John Langford
Paul Mineiro
376
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12 Jul 2022
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
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Jiafan He
Tianhao Wang
Yifei Min
Quanquan Gu
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313
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07 Jul 2022
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
International Conference on Machine Learning (ICML), 2022
Sanae Amani
Tor Lattimore
András Gyorgy
Lin F. Yang
FedML
241
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26 May 2022
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
International Conference on Learning Representations (ICLR), 2022
Jiawei Huang
Jinglin Chen
Li Zhao
Tao Qin
Nan Jiang
Tie-Yan Liu
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352
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A Benchmark for Low-Switching-Cost Reinforcement Learning
Shusheng Xu
Yancheng Liang
Yunfei Li
S. Du
Yi Wu
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179
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13 Dec 2021
Lipschitz Bandits with Batched Feedback
Yasong Feng
Zengfeng Huang
Tianyu Wang
470
18
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19 Oct 2021
Batched Thompson Sampling
Cem Kalkanli
Ayfer Özgür
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283
24
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01 Oct 2021
Design of Experiments for Stochastic Contextual Linear Bandits
Neural Information Processing Systems (NeurIPS), 2021
Andrea Zanette
Kefan Dong
Jonathan Lee
Emma Brunskill
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225
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Parallelizing Thompson Sampling
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Amin Karbasi
Vahab Mirrokni
M. Shadravan
233
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Parallelizing Contextual Bandits
Jeffrey Chan
Aldo Pacchiano
Nilesh Tripuraneni
Yun S. Song
Peter L. Bartlett
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290
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An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap
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Yuanhao Wang
Ruosong Wang
Sham Kakade
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454
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Online Convex Optimization with Continuous Switching Constraint
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Guanghui Wang
Yuanyu Wan
Tianbao Yang
Lijun Zhang
176
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Encrypted Linear Contextual Bandit
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Evrard Garcelon
Vianney Perchet
Matteo Pirotta
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234
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Batched Neural Bandits
ACM / IMS Journal of Data Science (JIDS), 2021
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Amin Karbasi
Khashayar Khosravi
Vahab Mirrokni
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140
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25 Feb 2021
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
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Chi Jin
Zhuoran Yang
Zhaoran Wang
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559
169
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06 Jan 2021
Impact of Representation Learning in Linear Bandits
Jiaqi Yang
Wei Hu
Jason D. Lee
S. Du
328
57
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Double Explore-then-Commit: Asymptotic Optimality and Beyond
Annual Conference Computational Learning Theory (COLT), 2020
Tianyuan Jin
Pan Xu
Xiaokui Xiao
Quanquan Gu
224
31
0
21 Feb 2020
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