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

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Showing 241–250 of 1262 papers

TitleStatusHype
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
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Benchmark Results

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