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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 701–750 of 1262 papers

TitleStatusHype
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
Inverse Contextual Bandits: Learning How Behavior Evolves over TimeCode0
Adapting to Misspecification in Contextual Bandits—0
Neural Contextual Bandits without RegretCode0
Model Selection for Generic Contextual Bandits—0
Dueling Bandits with Adversarial Sleeping—0
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit—0
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits—0
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions—0
Random Effect Bandits—0
Q-Learning Lagrange Policies for Multi-Action Restless BanditsCode0
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning—0
Reinforcement Learning for Physical Layer CommunicationsCode0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Smooth Sequential Optimisation with Delayed Feedback—0
Banker Online Mirror Descent—0
Guaranteed Fixed-Confidence Best Arm Identification in Multi-Armed Bandits: Simple Sequential Elimination Algorithms—0
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Fixed-Budget Best-Arm Identification in Structured Bandits—0
Scale Free Adversarial Multi Armed Bandits—0
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions—0
On Learning to Rank Long Sequences with Contextual Bandits—0
Multi-facet Contextual Bandits: A Neural Network PerspectiveCode0
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks—0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Fair Exploration via Axiomatic Bargaining—0
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits—0
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions—0
Addressing the Long-term Impact of ML Decisions via Policy RegretCode0
Invariant Policy Learning: A Causal PerspectiveCode0
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits—0
Parallelizing Contextual Bandits—0
Diffusion Approximations for Thompson Sampling—0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Stochastic Multi-Armed Bandits with Control Variates—0
Contextual Bandits with Sparse Data in Web setting—0
Policy Learning with Adaptively Collected DataCode0
Optimal Algorithms for Range Searching over Multi-Armed Bandits—0
Statistical Inference with M-Estimators on Adaptively Collected Data—0
Online certification of preference-based fairness for personalized recommender systems—0
Off-Policy Risk Assessment in Contextual Bandits—0
Censored Semi-Bandits for Resource Allocation—0
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
Leveraging Good Representations in Linear Contextual Bandits—0
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality—0
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information—0
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Benchmark Results

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