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 441–450 of 1262 papers

TitleStatusHype
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments—0
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency—0
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond—0
Practical Contextual Bandits with Feedback Graphs—0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
Show:102550
← PrevPage 45 of 127Next →

Benchmark Results

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