SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 655 papers

TitleStatusHype
Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking—0
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling—0
Sparse Nonparametric Contextual Bandits—0
Sparse Spectrum Gaussian Process for Bayesian Optimization—0
Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism—0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Stable Thompson Sampling: Valid Inference via Variance Inflation—0
Stage-wise Conservative Linear Bandits—0
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits—0
Stochastically Constrained Best Arm Identification with Thompson Sampling—0
Stochastic Neural Network with Kronecker Flow—0
Streaming kernel regression with provably adaptive mean, variance, and regularization—0
Surrogate modeling for Bayesian optimization beyond a single Gaussian process—0
Synthetically Controlled Bandits—0
Taming Non-stationary Bandits: A Bayesian Approach—0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
The Effect of Communication on Noncooperative Multiplayer Multi-Armed Bandit Problems—0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits—0
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle—0
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation—0
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling—0
The Sliding Regret in Stochastic Bandits: Discriminating Index and Randomized Policies—0
The Typical Behavior of Bandit Algorithms—0
Thompson Exploration with Best Challenger Rule in Best Arm Identification—0
Show:102550
← PrevPage 18 of 27Next →

No leaderboard results yet.