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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 241–250 of 655 papers

TitleStatusHype
Causal Bandits for Linear Structural Equation ModelsCode0
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Using Adaptive Experiments to Rapidly Help Students—0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems—0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Ranking In Generalized Linear BanditsCode0
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