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 441–450 of 1262 papers

TitleStatusHype
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Local Differential Privacy for Sequential Decision Making in a Changing Environment—0
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
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Benchmark Results

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