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 891–900 of 1262 papers

TitleStatusHype
Sequential Batch Learning in Finite-Action Linear Contextual Bandits—0
Power Constrained BanditsCode0
Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits—0
Hawkes Process Multi-armed Bandits for Disaster Search and Rescue—0
Hierarchical Adaptive Contextual Bandits for Resource Constraint based RecommendationCode1
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
Optimal No-regret Learning in Repeated First-price Auctions—0
Self-Supervised Contextual Bandits in Computer Vision—0
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
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Benchmark Results

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