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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 76–100 of 655 papers

TitleStatusHype
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback—0
Thompson Sampling Itself is Differentially Private—0
Scalable Exploration via Ensemble++Code0
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
Preferential Multi-Objective Bayesian Optimization—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Graph Neural Thompson Sampling—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation—0
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
A Bayesian Approach to Online PlanningCode1
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
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
On Bits and Bandits: Quantifying the Regret-Information Trade-offCode0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
No Algorithmic Collusion in Two-Player Blindfolded Game with Thompson Sampling—0
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making—0
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