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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 426–450 of 1262 papers

TitleStatusHype
Efficient Generalized Low-Rank Tensor Contextual Bandits—0
Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits—0
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning—0
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback—0
Efficient Reinforcement Learning via Initial Pure Exploration—0
Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits—0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation—0
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
Episodic Multi-armed Bandits—0
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
Equipping Experts/Bandits with Long-term Memory—0
Adapting to Misspecification in Contextual Bandits—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Estimation Considerations in Contextual Bandits—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Constant regret for sequence prediction with limited advice—0
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Benchmark Results

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