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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 776–800 of 1262 papers

TitleStatusHype
Lifelong Learning in Multi-Armed Bandits—0
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits—0
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Linear Contextual Bandits with Adversarial Corruptions—0
Linear Contextual Bandits with Interference—0
Linear Contextual Bandits with Knapsacks—0
Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms—0
LLMs-augmented Contextual Bandit—0
Local Clustering in Contextual Multi-Armed Bandits—0
Local Differential Privacy for Sequential Decision Making in a Changing Environment—0
(Locally) Differentially Private Combinatorial Semi-Bandits—0
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits—0
Making Contextual Decisions with Low Technical Debt—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
Maximum entropy exploration in contextual bandits with neural networks and energy based models—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations—0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits—0
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Meta-learners' learning dynamics are unlike learners'—0
Meta-Learning Adversarial Bandit Algorithms—0
Meta-Learning Adversarial Bandits—0
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Benchmark Results

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