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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 851–875 of 1262 papers

TitleStatusHype
Fair Algorithms for Multi-Agent Multi-Armed Bandits—0
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual BanditsCode0
Robust Multi-Agent Multi-Armed Bandits—0
Multi-Armed Bandits with Local Differential Privacy—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Continuous-Time Multi-Armed Bandits with Controlled Restarts—0
Offline Contextual Bandits with Overparameterized ModelsCode0
Online learning with Corrupted context: Corrupted Contextual Bandits—0
Approximating a Target Distribution using Weight QueriesCode0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
Open Problem: Model Selection for Contextual Bandits—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
Stochastic Bandits with Linear Constraints—0
Constrained regret minimization for multi-criterion multi-armed banditsCode0
Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach—0
Finding All ε-Good Arms in Stochastic BanditsCode0
Non-Stationary Off-Policy Optimization—0
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Bandits with Partially Observable Confounded Data—0
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
Efficient Contextual Bandits with Continuous ActionsCode1
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Benchmark Results

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