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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 176–200 of 1262 papers

TitleStatusHype
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Minimax-optimal trust-aware multi-armed bandits—0
Online Posterior Sampling with a Diffusion Prior—0
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs—0
On Lai's Upper Confidence Bound in Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Stabilizing the Kumaraswamy Distribution—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Linear Contextual Bandits with Interference—0
Second Order Bounds for Contextual Bandits with Function Approximation—0
Designing an Interpretable Interface for Contextual Bandits—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes—0
Faster Q-Learning Algorithms for Restless Bandits—0
Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network DesignCode0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Representative Arm Identification: A fixed confidence approach to identify cluster representatives—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Online Fair Division with Contextual Bandits—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
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Benchmark Results

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