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 711–720 of 1262 papers

TitleStatusHype
On Penalization in Stochastic Multi-armed Bandits—0
On Private and Robust Bandits—0
On Quantum Natural Policy Gradients—0
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits—0
On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits—0
On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits—0
On Speeding Up Language Model Evaluation—0
On Submodular Contextual Bandits—0
On the bias, risk and consistency of sample means in multi-armed bandits—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
Show:102550
← PrevPage 72 of 127Next →

Benchmark Results

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