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 851–860 of 1262 papers

TitleStatusHype
Fair Algorithms for Multi-Agent Multi-Armed Bandits—0
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual BanditsCode0
Robust Multi-Agent Multi-Armed Bandits—0
Multi-Armed Bandits with Local Differential Privacy—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Continuous-Time Multi-Armed Bandits with Controlled Restarts—0
Offline Contextual Bandits with Overparameterized ModelsCode0
Online learning with Corrupted context: Corrupted Contextual Bandits—0
Approximating a Target Distribution using Weight QueriesCode0
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