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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

Showing 231240 of 655 papers

TitleStatusHype
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits0
Using Adaptive Experiments to Rapidly Help Students0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis0
Ranking In Generalized Linear BanditsCode0
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs0
Langevin Monte Carlo for Contextual BanditsCode1
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes0
Thompson Sampling for (Combinatorial) Pure Exploration0
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