SOTAVerified

Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1025 of 1262 papers

TitleStatusHype
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits—0
Value Directed Exploration in Multi-Armed Bandits with Structured Priors—0
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency—0
Variance-Dependent Regret Lower Bounds for Contextual Bandits—0
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences—0
Vertical Federated Linear Contextual Bandits—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Bandit algorithms to emulate human decision making using probabilistic distortions—0
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits—0
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes—0
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task—0
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations—0
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits—0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
Zero-Inflated Bandits—0
Functional multi-armed bandit and the best function identification problems—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified