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 471–480 of 1262 papers

TitleStatusHype
Contextual Bandits and Optimistically Universal Learning—0
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
MABSplit: Faster Forest Training Using Multi-Armed BanditsCode0
Faster Maximum Inner Product Search in High Dimensions—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Networked Restless Bandits with Positive ExternalitiesCode0
Stochastic Rising BanditsCode0
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm ConfigurationCode0
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits—0
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Benchmark Results

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