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 701–725 of 1262 papers

TitleStatusHype
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
Hypothesis Transfer in Bandits by Weighted Models—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Imitation-Regularized Offline Learning—0
Imprecise Multi-Armed Bandits—0
Improved Algorithms for Adversarial Bandits with Unbounded Losses—0
Improved Algorithms for Misspecified Linear Markov Decision Processes—0
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback—0
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms—0
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs—0
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing—0
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit—0
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards—0
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits—0
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
Improving Offline Contextual Bandits with Distributional Robustness—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Incentivising Exploration and Recommendations for Contextual Bandits with Payments—0
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Benchmark Results

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