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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 101–125 of 655 papers

TitleStatusHype
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN—0
Preferential Multi-Objective Bayesian Optimization—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—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
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
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making—0
No Algorithmic Collusion in Two-Player Blindfolded Game with Thompson Sampling—0
Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
Thompson Sampling for Infinite-Horizon Discounted Decision Processes—0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
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