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Learning Unknown Service Rates in Queues: A Multiarmed Bandit Approach
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Learning Unknown Service Rates in Queues: A Multiarmed Bandit Approach (2020)Learning Unknown Service Rates in Queues:…Learning to Optimize via Posterior Sampling (2014)Learning to Optimize via Post…Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis (2012)Thompson Sampling: An Asympto…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations (2012)Combinatorial Network Optimiz…Combinatorial bandits (2012)Combinatorial banditsThe KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond (2011)The KL-UCB Algorithm for Boun…Multi‐Armed Bandit Allocation Indices (2011)Multi‐Armed Bandit Allocation…Analysis of Thompson Sampling for the multi-armed bandit problem (2011)Analysis of Thompson Sampling…Near-optimal Regret Bounds for Reinforcement Learning (2010)Near-optimal Regret Bounds fo…A modern Bayesian look at the multi‐armed bandit (2010)A modern Bayesian look at the…Exploration–exploitation tradeoff using variance estimates in multi-armed bandits (2009)Exploration–exploitation trad…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Dynamic Scheduling with Convex Delay Costs: The Generalized $c|mu$ Rule (1995)Dynamic Scheduling with Conve…Restless bandits: activity allocation in a changing world (1988)Restless bandits: activity al…Bandit Processes and Dynamic Allocation Indices (1979)Bandit Processes and Dynamic …When Lyapunov Drift Based Queue Scheduling Meets Adversarial Bandit Learning (2024)When Lyapunov Drift Based Que…The Nonstochastic Multiarmed Bandit Problem (2002)Regret Bounds for the Adaptive Control of Linear Quadratic Systems (2011)Confronting Deep Uncertainties in Risk Analysis (2012)A Tutorial on Thompson Sampling (2018)Multi-Armed Bandit Allocation Indices. (1990)An Empirical Evaluation of Thompson Sampling (2011)Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Introduction to Multi-Armed Bandits (2019)A Stochastic Approximation Method (1951)Prediction, Learning, and Games (2006)Introduction to Online Convex Optimization (2016)Introduction to Online Convex Optimization (2016)Some aspects of the sequential design of experiments (1952)Intrinsic motivation, curiosity, and learning (2016)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)A unified framework for stochastic optimization (2018)Online Decision Making with High-Dimensional Covariates (2019)Applied Statistical Decision Theory (1962)Applied Statistical Decision Theory. (1962)Legislative leviathan: party government in the House (1993)On an index policy for restless bandits (1990)The Psychology and Neuroscience of Curiosity (2015)Bandit Algorithms (2020)Information-seeking, curiosity, and attention: computational and neural mechanisms (2013)
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References

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  • 1995Dynamic Scheduling with Convex Delay Costs: The Generalized $c|mu$ Rule277 cited
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    • 2024When Lyapunov Drift Based Queue Scheduling Meets Adversarial Bandit Learning14 cited