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 761–770 of 1262 papers

TitleStatusHype
Parameterized Exploration—0
Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity—0
Personalized Course Sequence Recommendations—0
Perturbed-History Exploration in Stochastic Multi-Armed Bandits—0
Pessimism for Offline Linear Contextual Bandits using _p Confidence Sets—0
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Phasic Diversity Optimization for Population-Based Reinforcement Learning—0
Non-Stationary Off-Policy Optimization—0
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Benchmark Results

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