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 151–175 of 1262 papers

TitleStatusHype
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
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—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
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Representative Arm Identification: A fixed confidence approach to identify cluster representatives—0
Online Fair Division with Contextual Bandits—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities—0
Contextual Bandits for Unbounded Context Distributions—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
Reciprocal Learning—0
Hierarchical Multi-Armed Bandits for the Concurrent Intelligent Tutoring of Concepts and Problems of Varying Difficulty LevelsCode0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous AgentsCode0
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Benchmark Results

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