Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design

4 July 2020

Papers citing "Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design"

10 / 10 papers shown

Title
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Runze Zhao Yue Yu Adams Yiyue Zhu Chen Yang Dongruo Zhou 17 0 0 20 May 2025
Batched Stochastic Bandit for Nondegenerate Functions Yu Liu Yunlu Shu Tianyu Wang 54 0 0 09 May 2024
Generalized Linear Bandits with Limited Adaptivity Ayush Sawarni Nirjhar Das Siddharth Barman Gaurav Sinha 42 3 0 10 Apr 2024
Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data Ruiqi Zhang Andrea Zanette OffRL OnRL 42 7 0 10 Jul 2023
Active learning for data streams: a survey Davide Cacciarelli M. Kulahci 30 40 0 17 Feb 2023
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms Osama A. Hanna Lin F. Yang Christina Fragouli 29 11 0 08 Nov 2022
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning Zihan Zhang Yuhang Jiang Yuanshuo Zhou Xiangyang Ji OffRL 26 9 0 15 Oct 2022
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits Jiafan He Tianhao Wang Yifei Min Quanquan Gu FedML 45 32 0 07 Jul 2022
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap Yuanhao Wang Ruosong Wang Sham Kakade OffRL 44 43 0 23 Mar 2021
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints Chi Jin Zhuoran Yang Zhaoran Wang OffRL 122 167 0 06 Jan 2021