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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 111–120 of 655 papers

TitleStatusHype
Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits—0
Adaptively Learning to Select-Rank in Online Platforms—0
Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism—0
Posterior Sampling via Autoregressive Generation—0
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(T) Regret—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
On Bits and Bandits: Quantifying the Regret-Information Trade-offCode0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
No Algorithmic Collusion in Two-Player Blindfolded Game with Thompson Sampling—0
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