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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 151–175 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
Bayesian Quantile and Expectile Optimisation—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
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
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