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 226–250 of 1262 papers

TitleStatusHype
Towards Domain Adaptive Neural Contextual Bandits—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Asymptotically Optimal Regret for Black-Box Predict-then-Optimize—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits—0
Adaptively Learning to Select-Rank in Online Platforms—0
Optimal Batched Linear BanditsCode0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Global Rewards in Restless Multi-Armed Bandits—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Strategic Linear Contextual Bandits—0
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization—0
Understanding Memory-Regret Trade-Off for Streaming Stochastic Multi-Armed Bandits—0
Multi-Armed Bandits with Network InterferenceCode0
Causal Contextual Bandits with Adaptive ContextCode0
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff—0
Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits—0
Multi-Player Approaches for Dueling Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
Budgeted Recommendation with Delayed Feedback—0
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization—0
Imprecise Multi-Armed Bandits—0
Federated Combinatorial Multi-Agent Multi-Armed Bandits—0
Optimal Baseline Corrections for Off-Policy Contextual BanditsCode0
Show:102550
← PrevPage 10 of 51Next →

Benchmark Results

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