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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 301–350 of 655 papers

TitleStatusHype
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-armed Bandits with Cost Subsidy—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
New Insights into Bootstrapping for Bandits—0
No Algorithmic Collusion in Two-Player Blindfolded Game with Thompson Sampling—0
Nonparametric General Reinforcement Learning—0
Non-Stationary Bandit Learning via Predictive Sampling—0
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing—0
Non-Stationary Latent Bandits—0
No Regrets for Learning the Prior in Bandits—0
Observation-Free Attacks on Stochastic Bandits—0
On Adaptive Estimation for Dynamic Bernoulli Bandits—0
On Batch Bayesian Optimization—0
On Dynamic Pricing with Covariates—0
On Efficiency in Hierarchical Reinforcement Learning—0
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise—0
On Kernelized Multi-Armed Bandits with Constraints—0
On learning Whittle index policy for restless bandits with scalable regret—0
Online Algorithms For Parameter Mean And Variance Estimation In Dynamic Regression Models—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Online Causal Inference for Advertising in Real-Time Bidding Auctions—0
Online Learning and Distributed Control for Residential Demand Response—0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Online Learning of Energy Consumption for Navigation of Electric Vehicles—0
Online Learning of Network Bottlenecks via Minimax Paths—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials—0
On Online Learning in Kernelized Markov Decision Processes—0
On The Differential Privacy of Thompson Sampling With Gaussian Prior—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
On the Performance of Thompson Sampling on Logistic Bandits—0
On the Prior Sensitivity of Thompson Sampling—0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
On Thompson Sampling with Langevin Algorithms—0
On Frequentist Regret of Linear Thompson Sampling—0
Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment—0
Optimal Exploration is no harder than Thompson Sampling—0
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