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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 101–150 of 655 papers

TitleStatusHype
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Preferential Multi-Objective Bayesian Optimization—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents—0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
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
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(T) Regret—0
Posterior Sampling via Autoregressive Generation—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
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making—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
Efficient and Adaptive Posterior Sampling Algorithms for Bandits—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Online Learning of Decision Trees with Thompson SamplingCode0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
Meta Learning in Bandits within Shared Affine Subspaces—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
TS-RSR: A provably efficient approach for batch Bayesian Optimization—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits—0
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Tree Ensembles for Contextual Bandits—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games—0
Efficient Exploration for LLMs—0
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
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