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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 401–450 of 655 papers

TitleStatusHype
KLUCB Approach to Copeland Bandits—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Latent Bandits Revisited—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Sample Efficient Learning of Factored Embeddings of Tensor Fields—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Learning to Optimize Via Posterior Sampling—0
Learning to Price with Reference Effects—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach—0
Lenient Regret for Multi-Armed Bandits—0
Leveraging Demonstrations to Improve Online Learning: Quality Matters—0
Leveraging Offline Data from Similar Systems for Online Linear Quadratic Control—0
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits—0
Linear Bandit algorithms using the Bootstrap—0
Linear Thompson Sampling Revisited—0
Little Exploration is All You Need—0
Maillard Sampling: Boltzmann Exploration Done Optimally—0
Making RL with Preference-based Feedback Efficient via Randomization—0
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow—0
Optimization-Driven Adaptive Experimentation—0
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Meta Learning in Bandits within Shared Affine Subspaces—0
Metalearning Linear Bandits by Prior Update—0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks—0
Meta-Reinforcement Learning With Informed Policy Regularization—0
Meta-Thompson Sampling—0
Minimal Exploration in Structured Stochastic Bandits—0
TS-RSR: A provably efficient approach for batch Bayesian Optimization—0
Mixed-Variable Bayesian Optimization—0
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization—0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
Module-wise Adaptive Distillation for Multimodality Foundation Models—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Monte-Carlo tree search with uncertainty propagation via optimal transport—0
MOTS: Minimax Optimal Thompson Sampling—0
Multi-Agent Active Search using Detection and Location Uncertainty—0
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?—0
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes—0
Multi-dueling Bandits with Dependent Arms—0
Multi-Task Combinatorial Bandits for Budget Allocation—0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback—0
Neural Model-based Optimization with Right-Censored Observations—0
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