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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 1151–1175 of 1262 papers

TitleStatusHype
Finding All ε-Good Arms in Stochastic BanditsCode0
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit FeedbackCode0
Let's Get It Started: Fostering the Discoverability of New Releases on DeezerCode0
Ranking In Generalized Linear BanditsCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Finite-time Analysis of Globally Nonstationary Multi-Armed BanditsCode0
Online Limited Memory Neural-Linear Bandits with Likelihood MatchingCode0
Online Matching: A Real-time Bandit System for Large-scale RecommendationsCode0
Thompson Sampling for Contextual Bandits with Linear PayoffsCode0
Semiparametric Contextual BanditsCode0
Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network DesignCode0
Active Feature Selection for the Mutual Information CriterionCode0
Corralling a Band of Bandit AlgorithmsCode0
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
Correlated Multi-armed Bandits with a Latent Random SourceCode0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Linear Contextual Bandits with Hybrid Payoff: RevisitedCode0
Persistency of Excitation for Robustness of Neural NetworksCode0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsCode0
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual BanditsCode0
A Convex Framework for Confounding Robust InferenceCode0
From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon PerformanceCode0
From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance AdaptationCode0
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Benchmark Results

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