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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 801–825 of 1262 papers

TitleStatusHype
Asymptotic Convergence of Thompson Sampling—0
Towards Fundamental Limits of Multi-armed Bandits with Random Walk Feedback—0
Multi-armed Bandits with Cost Subsidy—0
Multi-Armed Bandits with Censored Consumption of Resources—0
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications—0
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism—0
Learning to Actively Learn: A Robust Approach—0
Tractable contextual bandits beyond realizability—0
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards—0
Online Semi-Supervised Learning with Bandit Feedback—0
Online Algorithm for Unsupervised Sequential Selection with Contextual Information—0
Achieving User-Side Fairness in Contextual Bandits—0
Quantile Bandits for Best Arms IdentificationCode0
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees—0
Stochastic Bandits with Vector Losses: Minimizing ^-Norm of Relative Losses—0
Asymptotic Randomised Control with applications to bandits—0
Multi-Armed Bandits with Dependent Arms—0
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
CorrAttack: Black-box Adversarial Attack with Structured Search—0
Neural Thompson SamplingCode1
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Contextual Bandits for adapting to changing User preferences over time—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
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Benchmark Results

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