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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 476–500 of 1262 papers

TitleStatusHype
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous ActionsCode0
PAC-Bayesian Offline Contextual Bandits With Guarantees—0
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees—0
Fast Beam Alignment via Pure Exploration in Multi-armed BanditsCode0
Optimal Contextual Bandits with Knapsacks under Realizability via Regression OraclesCode0
Vertical Federated Linear Contextual Bandits—0
Anytime-valid off-policy inference for contextual banditsCode1
Contextual bandits with concave rewards, and an application to fair ranking—0
Multi-agent Dynamic Algorithm ConfigurationCode1
Simulated Contextual Bandits for Personalization Tasks from Recommendation DatasetsCode0
Maximum entropy exploration in contextual bandits with neural networks and energy based models—0
Constant regret for sequence prediction with limited advice—0
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits—0
Replicable Bandits—0
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs—0
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits—0
Off-Policy Risk Assessment in Markov Decision Processes—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Towards Robust Off-Policy Evaluation via Human Inputs—0
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems—0
Risk-aware linear bandits with convex loss—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits—0
Multi-Armed Bandits with Self-Information Rewards—0
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Benchmark Results

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