SOTAVerified

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 676–700 of 1262 papers

TitleStatusHype
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function—0
Maximizing and Satisficing in Multi-armed Bandits with Graph InformationCode0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
Finite-time Analysis of Globally Nonstationary Multi-Armed BanditsCode0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
An Analysis of Reinforcement Learning for Malaria Control—0
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
Inverse Contextual Bandits: Learning How Behavior Evolves over TimeCode0
Adapting to Misspecification in Contextual Bandits—0
Model Selection for Generic Contextual Bandits—0
Neural Contextual Bandits without RegretCode0
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination—0
Dueling Bandits with Adversarial Sleeping—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
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Benchmark Results

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