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 531–540 of 1262 papers

TitleStatusHype
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits—0
Query-Reward Tradeoffs in Multi-Armed Bandits—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Batched Bandits with Crowd Externalities—0
Cost-Aware Optimal Pairwise Pure Exploration—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Adaptive Endpointing with Deep Contextual Multi-armed Bandits—0
Corruption-robust exploration in episodic reinforcement learning—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Banker Online Mirror Descent—0
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Benchmark Results

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