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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 151–160 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
Bayesian Quantile and Expectile Optimisation—0
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