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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 276–300 of 1262 papers

TitleStatusHype
On Quantum Natural Policy Gradients—0
Contextual Bandits with Stage-wise Constraints—0
Let's Get It Started: Fostering the Discoverability of New Releases on DeezerCode0
Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach—0
Optimal cross-learning for contextual bandits with unknown context distributions—0
Foundations of Reinforcement Learning and Interactive Decision Making—0
Best-of-Both-Worlds Linear Contextual Bandits—0
Harnessing the Power of Federated Learning in Federated Contextual BanditsCode0
Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed Bandits—0
Zero-Inflated Bandits—0
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits—0
Neural Contextual Bandits for Personalized Recommendation—0
In-Context Reinforcement Learning for Variable Action SpacesCode1
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Distribution-Dependent Rates for Multi-Distribution Learning—0
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration—0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Risk-Aware Continuous Control with Neural Contextual BanditsCode0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents—0
Contextual Bandits with Online Neural Regression—0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Marginal Density Ratio for Off-Policy Evaluation in Contextual BanditsCode0
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Benchmark Results

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