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 121–130 of 1262 papers

TitleStatusHype
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
PageRank Bandits for Link PredictionCode0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation—0
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints—0
Optimal Streaming Algorithms for Multi-Armed Bandits—0
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits—0
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Benchmark Results

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