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 781–790 of 1262 papers

TitleStatusHype
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information—0
Be Greedy in Multi-Armed Bandits—0
Online Learning under Adversarial Corruptions—0
Online Limited Memory Neural-Linear Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks—0
Lifelong Learning in Multi-Armed Bandits—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Relational Boosted BanditsCode0
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Benchmark Results

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