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 301–310 of 1262 papers

TitleStatusHype
Query-Efficient Correlation Clustering with Noisy Oracle—0
Multi-Armed Bandits with Interference—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual BanditsCode0
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints—0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
On Quantum Natural Policy Gradients—0
Contextual Bandits with Stage-wise Constraints—0
Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach—0
Let's Get It Started: Fostering the Discoverability of New Releases on DeezerCode0
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Benchmark Results

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