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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 151–200 of 655 papers

TitleStatusHype
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Combinatorial Neural Bandits—0
Combining Bayesian Optimization and Lipschitz Optimization—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret—0
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio—0
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection—0
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints—0
Constrained Thompson Sampling for Wireless Link Optimization—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
Context Attentive Bandits: Contextual Bandit with Restricted Context—0
Context Attribution with Multi-Armed Bandit Optimization—0
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling—0
Contextual Bandits for Advertising Budget Allocation—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Contextual Thompson Sampling via Generation of Missing Data—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Convolutional Monte Carlo Rollouts in Go—0
Cost Aware Asynchronous Multi-Agent Active Search—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Asymptotically Optimal Bandits under Weighted Information—0
Counterfactual Data-Fusion for Online Reinforcement Learners—0
Counterfactual Inference under Thompson Sampling—0
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits—0
Cover Tree Bayesian Reinforcement Learning—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Asymptotic Convergence of Thompson Sampling—0
Debiasing Samples from Online Learning Using Bootstrap—0
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents—0
Deciding What to Learn: A Rate-Distortion Approach—0
Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine—0
Deep Active Ensemble Sampling For Image Classification—0
Bayesian Quantile and Expectile Optimisation—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
Deep Contextual Multi-armed Bandits—0
Deep Exploration for Recommendation Systems—0
Deep Hierarchy in Bandits—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
Differentially Private Federated Bayesian Optimization with Distributed Exploration—0
Diffusion Approximations for Thompson Sampling—0
A Copula approach for hyperparameter transfer learning—0
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