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
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits—0
Neural Network Retraining for Model Serving—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Thompson Sampling for Linearly Constrained BanditsCode0
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
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
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Benchmark Results

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