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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 501–550 of 655 papers

TitleStatusHype
Policy Gradient Optimization of Thompson Sampling Policies—0
Position-Based Multiple-Play Bandits with Thompson Sampling—0
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Posterior Sampling via Autoregressive Generation—0
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection—0
Preferential Multi-Objective Bayesian Optimization—0
Prior-free and prior-dependent regret bounds for Thompson Sampling—0
Probabilistic Inference in Reinforcement Learning Done Right—0
Profitable Bandits—0
QoS-Aware Multi-Armed Bandits—0
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors—0
Random Effect Bandits—0
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization—0
Randomised Bayesian Least-Squares Policy Iteration—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Regenerative Particle Thompson Sampling—0
Regret Analysis of Bandit Problems with Causal Background Knowledge—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Regret Bounds for Information-Directed Reinforcement Learning—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Reinforcement Learning with Trajectory Feedback—0
Remote Contextual Bandits—0
Residual Bootstrap Exploration for Bandit Algorithms—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Risk and optimal policies in bandit experiments—0
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs—0
Risk-Constrained Thompson Sampling for CVaR Bandits—0
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers—0
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments—0
Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks—0
Safe Linear Leveling Bandits—0
Safe Linear Thompson Sampling with Side Information—0
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Sampling Acquisition Functions for Batch Bayesian Optimization—0
Satisficing in Time-Sensitive Bandit Learning—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable Generalized Linear Bandits: Online Computation and Hashing—0
Scalable Neural Contextual Bandit for Recommender Systems—0
Scalable regret for learning to control network-coupled subsystems with unknown dynamics—0
Scalable Thompson Sampling using Sparse Gaussian Process Models—0
Scalable Thompson Sampling via Optimal Transport—0
Scaling Multi-Armed Bandit Algorithms—0
Screening for an Infectious Disease as a Problem in Stochastic Control—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
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