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Sequential Batch Learning in Finite-Action Linear Contextual Bandits
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Sequential Batch Learning in Finite-Action Linear Contextual Bandits (2020)Sequential Batch Learning in Finite-Actio…The Big Data Newsvendor: Practical Insights from Machine Learning (2018)The Big Data Newsvendor: Prac…Online Network Revenue Management Using Thompson Sampling (2018)Online Network Revenue Manage…Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments (2017)Customer Acquisition via Disp…Batch learning from logged bandit feedback through counterfactual risk minimization (2015)Batch learning from logged ba…Learning to Optimize via Posterior Sampling (2014)Learning to Optimize via Post…An Information-Theoretic Analysis of Thompson Sampling (2014)An Information-Theoretic Anal…Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Thompson Sampling for Context…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…Further Optimal Regret Bounds for Thompson Sampling (2012)Further Optimal Regret Bounds…Doubly Robust Policy Evaluation and Learning (2011)Doubly Robust Policy Evaluati…Contextual Bandits with Linear Payoff Functions (2011)Contextual Bandits with Linea…Parametric Bandits: The Generalized Linear Case (2010)Parametric Bandits: The Gener…Linearly Parameterized Bandits (2010)Linearly Parameterized BanditsUsing confidence bounds for exploitation-exploration trade-offs (2003)Using confidence bounds for e…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Some aspects of the sequential design of experiments (1952)Some aspects of the sequentia…The Nonstochastic Multiarmed Bandit Problem (2002)Stochastic Linear Optimization Under Bandit Feedback (2008)Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2011)Collaborative Filtering Bandits (2016)A Stochastic Approximation Method (1951)Bandit Processes and Dynamic Allocation Indices (1979)Prediction, Learning, and Games (2006)Introduction to Online Convex Optimization (2016)Introduction to Online Convex Optimization (2016)Introduction to Multi-Armed Bandits (2019)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)A unified framework for stochastic optimization (2018)An Empirical Evaluation of Thompson Sampling (2011)Studies in Mathematical Theory of Inventory and Production (1959)The Distribution Free Newsboy Problem: Review and Extensions (1993)How Research in Production and Operations Management May Evolve in the Era of Big Data (2017)Smart “Predict, then Optimize” (2021)Frameworks and Results in Distributionally Robust Optimization (2022)Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (2014)The self-normalized estimator for counterfactual learning (2015)Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations (2012)A Tutorial on Thompson Sampling (2018)Online Decision Making with High-Dimensional Covariates (2019)Improved Algorithms for Linear Stochastic Bandits (2011)Bandit Algorithms (2020)
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References

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  • 1952Some aspects of the sequential design of experiments2,183 cited
  • 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems1,410 cited
  • 2003Using confidence bounds for exploitation-exploration trade-offs1,189 cited
  • 2012Thompson Sampling for Contextual Bandits with Linear Payoffs600 cited
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  • 2018The Big Data Newsvendor: Practical Insights from Machine Learning510 cited
  • 2014Learning to Optimize via Posterior Sampling494 cited
  • 2010Linearly Parameterized Bandits428 cited
  • 2011Doubly Robust Policy Evaluation and Learning383 cited
  • 2012Further Optimal Regret Bounds for Thompson Sampling308 cited
  • 2014An Information-Theoretic Analysis of Thompson Sampling264 cited
  • 2017Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments261 cited
  • 2018Online Network Revenue Management Using Thompson Sampling243 cited
  • 2010Parametric Bandits: The Generalized Linear Case241 cited
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Batch learning from logged bandit feedback through counterfactual risk minimization
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