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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

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
Designing an Interpretable Interface for Contextual Bandits—0
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes—0
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits—0
Delegating via Quitting Games—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
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection—0
Episodic Multi-armed Bandits—0
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits—0
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
Estimation Considerations in Contextual Bandits—0
Adaptive Exploration in Linear Contextual Bandit—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
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Benchmark Results

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