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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 26–50 of 1262 papers

TitleStatusHype
Adaptive, Robust and Scalable Bayesian Filtering for Online Learning—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms—0
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
On the Problem of Best Arm Retention—0
Learning-Based User Association for MmWave Vehicular Networks With Kernelized Contextual Bandits—0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
A Classification View on Meta Learning Bandits—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
Antithetic Sampling for Top-k Shapley IdentificationCode0
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
MultiScale Contextual Bandits for Long Term Objectives—0
Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates—0
NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis PredictionCode0
Sparse Nonparametric Contextual Bandits—0
Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety AssessmentCode1
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Variance-Dependent Regret Lower Bounds for Contextual Bandits—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
Locally Private Nonparametric Contextual Multi-armed BanditsCode0
Multiplayer Information Asymmetric Contextual Bandits—0
Cost-Aware Optimal Pairwise Pure Exploration—0
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Benchmark Results

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