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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 751–800 of 1262 papers

TitleStatusHype
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits—0
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach—0
Learning-Based User Association for MmWave Vehicular Networks With Kernelized Contextual Bandits—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Learning diverse rankings with multi-armed bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning How to Price Charging in Electric Ride-Hailing Markets—0
Learning in Generalized Linear Contextual Bandits with Stochastic Delays—0
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules—0
Learning Multiple Tasks in Parallel with a Shared Annotator—0
Learning Personalized Decision Support Policies—0
Learning to Actively Learn: A Robust Approach—0
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems—0
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints—0
Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Learning to Search Better Than Your Teacher—0
Learning to Use Learners' Advice—0
Lenient Regret for Multi-Armed Bandits—0
Lessons from Contextual Bandit Learning in a Customer Support Bot—0
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data—0
Leveraging Good Representations in Linear Contextual Bandits—0
Leveraging heterogeneous spillover in maximizing contextual bandit rewards—0
Leveraging User-Triggered Supervision in Contextual Bandits—0
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