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 71–80 of 1262 papers

TitleStatusHype
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Adaptive Exploration in Linear Contextual Bandit—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
An Adaptive Method for Contextual Stochastic Multi-armed Bandits with Rewards Generated by a Linear Dynamical System—0
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits—0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
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Benchmark Results

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