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 276–300 of 1262 papers

TitleStatusHype
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Linear Bandits with Stochastic Delayed Feedback—0
Contextual Bandits with Arm Request Costs and Delays—0
Contextual Bandits with Budgeted Information Reveal—0
Contextual bandits with concave rewards, and an application to fair ranking—0
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
Contextual Bandits with Cross-learning—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Asymptotic Randomised Control with applications to bandits—0
Contextual Bandits with Knapsacks for a Conversion Model—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Contextual Bandits with Online Neural Regression—0
Contextual Bandits with Random Projection—0
Contextual Bandits with Side-Observations—0
Contextual Bandits with Similarity Information—0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Contextual Bandits with Sparse Data in Web setting—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Contextual Bandits with Stage-wise Constraints—0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Contextual Causal Bayesian Optimisation—0
Context-Aware Bandits—0
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Benchmark Results

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