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 1011–1020 of 1262 papers

TitleStatusHype
Achieving Fairness in Stochastic Multi-armed Bandit Problem—0
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits—0
Data Poisoning Attacks on Stochastic Bandits—0
Lessons from Contextual Bandit Learning in a Customer Support Bot—0
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits—0
Meta-learners' learning dynamics are unlike learners'—0
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without—0
Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems—0
Introduction to Multi-Armed BanditsCode0
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication—0
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Benchmark Results

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