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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 451–500 of 655 papers

TitleStatusHype
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Randomized Exploration for Non-Stationary Stochastic Linear BanditsCode0
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling—0
Ordinal Bayesian Optimisation—0
Thompson Sampling and Approximate Inference—0
Thompson Sampling for Multinomial Logit Contextual BanditsCode0
Bayesian Optimization for Categorical and Category-Specific Continuous InputsCode0
Automatic Ensemble Learning for Online Influence Maximization—0
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood StructuresCode0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Safe Linear Thompson Sampling with Side Information—0
On Online Learning in Kernelized Markov Decision Processes—0
On Batch Bayesian Optimization—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling via Local UncertaintyCode0
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification—0
Thompson Sampling in Non-Episodic Restless Bandits—0
Regret Analysis of Bandit Problems with Causal Background Knowledge—0
Old Dog Learns New Tricks: Randomized UCB for Bandit ProblemsCode0
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
A Copula approach for hyperparameter transfer learning—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits—0
Online Causal Inference for Advertising in Real-Time Bidding Auctions—0
A Batched Multi-Armed Bandit Approach to News Headline Testing—0
A Bayesian Choice Model for Eliminating Feedback Loops—0
Thompson Sampling with Approximate Inference—0
Scaling Multi-Armed Bandit Algorithms—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop PlanningCode0
Thompson Sampling on Symmetric α-Stable Bandits—0
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments—0
Mixed-Variable Bayesian Optimization—0
Bandit Learning for Diversified Interactive Recommendation—0
Thompson Sampling for Adversarial Bit Prediction—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Sparse Spectrum Gaussian Process for Bayesian Optimization—0
Stochastic Neural Network with Kronecker Flow—0
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation—0
Regret Bounds for Thompson Sampling in Episodic Restless Bandit ProblemsCode0
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio—0
Feedback graph regret bounds for Thompson Sampling and UCB—0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Adaptive Sensor Placement for Continuous Spaces—0
On the Performance of Thompson Sampling on Logistic Bandits—0
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson SamplingCode0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
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