Cover Image

Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jose, Sharu Theresa, Moothedath, Shana
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural Contextual Bandits Under Delayed Feedback Constraints
by: Moghimi, Mohammadali, et al.
Published: (2025)

Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
by: Lin, Jiabin, et al.
Published: (2024)

Federated Learning for Heterogeneous Bandits with Unobserved Contexts
by: Lin, Jiabin, et al.
Published: (2023)

Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
by: Lin, Jiabin, et al.
Published: (2024)

Learning Shared Representations for Multi-Task Linear Bandits
by: Lin, Jiabin, et al.
Published: (2026)

Multi-Task Representation Learning for Conservative Linear Bandits
by: Lin, Jiabin, et al.
Published: (2026)

An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
by: Gouverneur, Amaury, et al.
Published: (2024)

Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis
by: Georgiou, Petros, et al.
Published: (2024)

Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox
by: Zhang, Raymond, et al.
Published: (2024)

Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
by: Kang, Donghwa, et al.
Published: (2025)

Diffusion-based Decentralized Federated Multi-Task Representation Learning
by: Kang, Donghwa, et al.
Published: (2025)

Byzantine Resilient Federated Multi-Task Representation Learning
by: Le, Tuan, et al.
Published: (2025)

Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
by: Guo, Yaoze, et al.
Published: (2026)

Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis
by: Li, Hao, et al.
Published: (2024)

Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits
by: Huang, Zhiming, et al.
Published: (2026)

Thompson Sampling-like Algorithms for Stochastic Rising Bandits
by: Fiandri, Marco, et al.
Published: (2025)

Quantum-Enhanced Neural Contextual Bandit Algorithms
by: Huang, Yuqi, et al.
Published: (2026)

An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces
by: Gouverneur, Amaury, et al.
Published: (2025)

Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
by: Sadhu, Abhishek, et al.
Published: (2026)

On Regret Bounds of Thompson Sampling for Bayesian Optimization
by: Takeno, Shion, et al.
Published: (2026)

Thompson Sampling in Partially Observable Contextual Bandits
by: Park, Hongju, et al.
Published: (2024)

Sample Complexity of Composite Quantum Hypothesis Testing
by: Simpson, Jacob Paul, et al.
Published: (2026)

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
by: Gouverneur, Amaury, et al.
Published: (2024)

Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
by: Cassel, Asaf, et al.
Published: (2024)

Individual Regret in Cooperative Stochastic Multi-Armed Bandits
by: Barnea, Idan, et al.
Published: (2024)

Thompson Sampling for Multi-Objective Linear Contextual Bandit
by: Park, Somangchan, et al.
Published: (2025)

p-Mean Regret for Stochastic Bandits
by: Krishna, Anand, et al.
Published: (2024)

Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes
by: Sharma, Nihal, et al.
Published: (2021)

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
by: Sandberg, Jack, et al.
Published: (2025)

Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
by: Moradipari, Ahmadreza, et al.
Published: (2023)

Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits
by: Jeong, Woojin, et al.
Published: (2024)

Feel-Good Thompson Sampling for Contextual Dueling Bandits
by: Li, Xuheng, et al.
Published: (2024)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
by: Roy, Somjit, et al.
Published: (2026)

Active Context Selection Improves Simple Regret in Contextual Bandits
by: Shahverdikondori, Mohammad, et al.
Published: (2026)

No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
by: Bayrooti, Jasmine, et al.
Published: (2025)

Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit
by: Zhou, Julien, et al.
Published: (2024)

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback
by: Della Vecchia, Riccardo, et al.
Published: (2023)

Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
by: Li, Yingru, et al.
Published: (2024)

Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
by: Di, Qiwei, et al.
Published: (2023)

Variance-Aware Feel-Good Thompson Sampling for Contextual Bandits
by: Li, Xuheng, et al.
Published: (2025)