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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 401–450 of 1262 papers

TitleStatusHype
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
Dynamic pricing and assortment under a contextual MNL demand—0
Dynamic Pricing with Limited Supply—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Early Stopping in Contextual Bandits and Inferences—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Efficient Action Poisoning Attacks on Linear Contextual Bandits—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems—0
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts—0
Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits—0
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits—0
Efficient Automatic CASH via Rising Bandits—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Efficient Contextual Bandits in Non-stationary Worlds—0
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget—0
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression—0
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits—0
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
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