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Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
v1v2 (latest)

Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits

30 March 2019
Yingkai Li
Yining Wang
Yuanshuo Zhou
ArXiv (abs)PDFHTML

Papers citing "Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits"

44 / 44 papers shown
Title
Optimal and Practical Batched Linear Bandit Algorithm
Optimal and Practical Batched Linear Bandit Algorithm
Sanghoon Yu
Min-hwan Oh
67
0
0
11 Jul 2025
Experimental Design for Semiparametric Bandits
Experimental Design for Semiparametric Bandits
Seok-Jin Kim
Gi-Soo Kim
Min-hwan Oh
70
0
0
16 Jun 2025
Contextual Online Uncertainty-Aware Preference Learning for Human Feedback
Contextual Online Uncertainty-Aware Preference Learning for Human Feedback
Nan Lu
Ethan X. Fang
Junwei Lu
616
0
0
27 Apr 2025
Parameter-Adaptive Dynamic Pricing
Xueping Gong
Jiheng Zhang
144
0
0
02 Mar 2025
Near-Optimal Private Learning in Linear Contextual Bandits
Near-Optimal Private Learning in Linear Contextual Bandits
Fan Chen
Jiachun Li
Alexander Rakhlin
D. Simchi-Levi
154
1
0
18 Feb 2025
Linear Causal Bandits: Unknown Graph and Soft Interventions
Linear Causal Bandits: Unknown Graph and Soft Interventions
Zirui Yan
A. Tajer
CML
116
1
0
04 Nov 2024
Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models
Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models
Xueping Gong
Wei You
Jiheng Zhang
93
0
0
24 Jun 2024
Linear Contextual Bandits with Hybrid Payoff: Revisited
Linear Contextual Bandits with Hybrid Payoff: Revisited
Nirjhar Das
Gaurav Sinha
99
3
0
14 Jun 2024
Causally Abstracted Multi-armed Bandits
Causally Abstracted Multi-armed Bandits
Fabio Massimo Zennaro
Nicholas Bishop
Joel Dyer
Yorgos Felekis
Anisoara Calinescu
Michael Wooldridge
Theodoros Damoulas
163
5
0
26 Apr 2024
Prior-dependent analysis of posterior sampling reinforcement learning
  with function approximation
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
Yingru Li
Zhi-Quan Luo
94
0
0
17 Mar 2024
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki
Alberto Rumi
Taira Tsuchiya
Fabio Vitale
Nicolò Cesa-Bianchi
151
8
0
24 Dec 2023
Communication-Efficient Federated Non-Linear Bandit Optimization
Communication-Efficient Federated Non-Linear Bandit Optimization
Chuanhao Li
Chong Liu
Yu Wang
FedML
91
1
0
03 Nov 2023
Federated Linear Bandits with Finite Adversarial Actions
Federated Linear Bandits with Finite Adversarial Actions
Li Fan
Ruida Zhou
Chao Tian
Cong Shen
FedML
157
3
0
02 Nov 2023
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving
  Contexts
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Chaoqi Wang
Ziyu Ye
Zhe Feng
Ashwinkumar Badanidiyuru
Haifeng Xu
76
1
0
25 Sep 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual
  Bandits
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
Haolin Liu
Chen-Yu Wei
Julian Zimmert
92
11
0
02 Sep 2023
CO-BED: Information-Theoretic Contextual Optimization via Bayesian
  Experimental Design
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
Desi R. Ivanova
Joel Jennings
Tom Rainforth
Cheng Zhang
Adam Foster
148
3
0
27 Feb 2023
No-Regret Linear Bandits beyond Realizability
No-Regret Linear Bandits beyond Realizability
Chong Liu
Ming Yin
Yu Wang
51
2
0
26 Feb 2023
Provably Efficient Reinforcement Learning via Surprise Bound
Provably Efficient Reinforcement Learning via Surprise Bound
Hanlin Zhu
Ruosong Wang
Jason D. Lee
OffRL
74
5
0
22 Feb 2023
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement
  Learning: Adaptivity and Computational Efficiency
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
Heyang Zhao
Jiafan He
Dongruo Zhou
Tong Zhang
Quanquan Gu
131
30
0
21 Feb 2023
Global Optimization with Parametric Function Approximation
Global Optimization with Parametric Function Approximation
Chong Liu
Yu Wang
167
7
0
16 Nov 2022
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear
  Bandit Algorithms
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
Osama A. Hanna
Lin F. Yang
Christina Fragouli
148
15
0
08 Nov 2022
Lifelong Bandit Optimization: No Prior and No Regret
Lifelong Bandit Optimization: No Prior and No Regret
Felix Schur
Parnian Kassraie
Jonas Rothfuss
Andreas Krause
148
3
0
27 Oct 2022
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction
  Design
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
Rui Ai
Boxiang Lyu
Zhaoran Wang
Zhuoran Yang
Michael I. Jordan
117
4
0
19 Oct 2022
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual
  Bandits
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits
Wonyoung Hedge Kim
Kyungbok Lee
M. Paik
138
15
0
15 Sep 2022
Dual Instrumental Method for Confounded Kernelized Bandits
Dual Instrumental Method for Confounded Kernelized Bandits
Xueping Gong
Jiheng Zhang
132
1
0
07 Sep 2022
A Simple and Provably Efficient Algorithm for Asynchronous Federated
  Contextual Linear Bandits
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
Jiafan He
Tianhao Wang
Yifei Min
Quanquan Gu
FedML
119
40
0
07 Jul 2022
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear
  Contextual Bandits
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Wonyoung Hedge Kim
M. Paik
Min-whan Oh
113
6
0
11 Jun 2022
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making
Kefan Dong
Tengyu Ma
165
9
0
06 Jun 2022
Provably and Practically Efficient Neural Contextual Bandits
Provably and Practically Efficient Neural Contextual Bandits
Sudeep Salgia
Sattar Vakili
Qing Zhao
114
9
0
31 May 2022
Computationally Efficient Horizon-Free Reinforcement Learning for Linear
  Mixture MDPs
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
Dongruo Zhou
Quanquan Gu
156
49
0
23 May 2022
Provably Efficient Kernelized Q-Learning
Provably Efficient Kernelized Q-Learning
Shuang Liu
H. Su
MLT
128
4
0
21 Apr 2022
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment
  Effect Oracles
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles
Aldo G. Carranza
Sanath Kumar Krishnamurthy
Susan Athey
113
1
0
30 Mar 2022
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
Jiaqi Yang
Qi Lei
Jason D. Lee
S. Du
135
16
0
29 Mar 2022
Truncated LinUCB for Stochastic Linear Bandits
Truncated LinUCB for Stochastic Linear Bandits
Yanglei Song
Meng zhou
338
0
0
23 Feb 2022
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed
  Bandits
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits
Maximilian Egger
Rawad Bitar
Antonia Wachter-Zeh
Deniz Gunduz
FedML
158
1
0
16 Feb 2022
Rate-matching the regret lower-bound in the linear quadratic regulator
  with unknown dynamics
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics
Feicheng Wang
Lucas Janson
85
1
0
11 Feb 2022
Optimal Regret Is Achievable with Bounded Approximate Inference Error:
  An Enhanced Bayesian Upper Confidence Bound Framework
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Ziyi Huang
Henry Lam
A. Meisami
Haofeng Zhang
145
4
0
31 Jan 2022
Improved Regret Analysis for Variance-Adaptive Linear Bandits and
  Horizon-Free Linear Mixture MDPs
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
Yeoneung Kim
Insoon Yang
Kwang-Sung Jun
131
40
0
05 Nov 2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and
  Triangular Discrimination
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Dylan J. Foster
A. Krishnamurthy
135
50
0
05 Jul 2021
A Simple Approach for Non-stationary Linear Bandits
A Simple Approach for Non-stationary Linear Bandits
Peng Zhao
Lijun Zhang
Yuan Jiang
Zhi Zhou
119
89
0
09 Mar 2021
The Elliptical Potential Lemma for General Distributions with an
  Application to Linear Thompson Sampling
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling
N. Hamidi
Mohsen Bayati
77
1
0
16 Feb 2021
The Elliptical Potential Lemma Revisited
The Elliptical Potential Lemma Revisited
Alexandra Carpentier
Claire Vernade
Yasin Abbasi-Yadkori
219
21
0
20 Oct 2020
Reinforcement Learning with General Value Function Approximation:
  Provably Efficient Approach via Bounded Eluder Dimension
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Ruosong Wang
Ruslan Salakhutdinov
Lin F. Yang
132
55
0
21 May 2020
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for
  Contextual Bandits under Realizability
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability
D. Simchi-Levi
Yunzong Xu
OffRL
520
115
0
28 Mar 2020
1