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 551–600 of 655 papers

TitleStatusHype
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Thompson Sampling for Dynamic Pricing—0
Information Directed Sampling and Bandits with Heteroscedastic Noise—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
On Adaptive Estimation for Dynamic Bernoulli Bandits—0
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds—0
Efficient exploration with Double Uncertain Value Networks—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Estimating prediction error for complex samples—0
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
The Effect of Communication on Noncooperative Multiplayer Multi-Armed Bandit Problems—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Minimal Exploration in Structured Stochastic Bandits—0
Sequential Matrix Completion—0
A study of Thompson Sampling with Parameter h—0
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach—0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits—0
Bayesian bandits: balancing the exploration-exploitation tradeoff via double samplingCode0
Variational inference for the multi-armed contextual banditCode0
Learning to Price with Reference Effects—0
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors—0
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Streaming kernel regression with provably adaptive mean, variance, and regularization—0
Counterfactual Data-Fusion for Online Reinforcement Learners—0
Taming Non-stationary Bandits: A Bayesian Approach—0
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret—0
Calibrated Fairness in Bandits—0
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space—0
Thompson Sampling for the MNL-Bandit—0
Scalable Generalized Linear Bandits: Online Computation and Hashing—0
Asynchronous Parallel Bayesian Optimisation via Thompson SamplingCode0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
AIXIjs: A Software Demo for General Reinforcement LearningCode0
Ensemble Sampling—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Context Attentive Bandits: Contextual Bandit with Restricted Context—0
Multi-dueling Bandits with Dependent Arms—0
Mostly Exploration-Free Algorithms for Contextual BanditsCode0
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Thompson Sampling for Linear-Quadratic Control Problems—0
Horde of Bandits using Gaussian Markov Random Fields—0
QoS-Aware Multi-Armed Bandits—0
Show:102550
← PrevPage 12 of 14Next →

No leaderboard results yet.