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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 551–575 of 1262 papers

TitleStatusHype
Contextual Bandits for Unbounded Context Distributions—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
High-dimensional Linear Bandits with Knapsacks—0
High-dimensional Nonparametric Contextual Bandit Problem—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Encrypted Linear Contextual Bandit—0
Honor Among Bandits: No-Regret Learning for Online Fair Division—0
Horde of Bandits using Gaussian Markov Random Fields—0
How Does Variance Shape the Regret in Contextual Bandits?—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information—0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Imitation-Regularized Offline Learning—0
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Improved Algorithms for Adversarial Bandits with Unbounded Losses—0
Improved Algorithms for Misspecified Linear Markov Decision Processes—0
From Bandits to Experts: On the Value of Side-Observations—0
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Benchmark Results

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