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Learning to Rank
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Learning to Rank (2015)Learning to RankUnimodal Bandits: Regret Lower Bounds and Optimal Algorithms (2014)Unimodal Bandits: Regret Lowe…Ranked bandits in metric spaces: learning diverse rankings over large document collections (2013)Ranked bandits in metric spac…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…The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond (2011)The KL-UCB Algorithm for Boun…Linear Submodular Bandits and their Application to Diversified Retrieval (2011)Linear Submodular Bandits and…Bandit processes and dynamic allocation indices (2010)Bandit processes and dynamic …Interactively optimizing information retrieval systems as a dueling bandits problem (2009)Interactively optimizing info…Stochastic Linear Optimization Under Bandit Feedback (2008)Stochastic Linear Optimizatio…Multi-armed bandits in metric spaces (2008)Multi-armed bandits in metric…Learning diverse rankings with multi-armed bandits (2008)Learning diverse rankings wit…Bandits for Taxonomies: A Model-based Approach (2007)Bandits for Taxonomies: A Mod…Online convex optimization in the bandit setting: gradient descent without a gradient (2004)Online convex optimization in…The Continuum-Armed Bandit Problem (1995)The Continuum-Armed Bandit Pr…Bandit Processes and Dynamic Allocation Indices (1979)Bandit Processes and Dynamic …Some aspects of the sequential design of experiments (1952)Some aspects of the sequentia…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Exploration–exploitation tradeoff using variance estimates in multi-armed bandits (2009)Optimistic bayesian sampling in contextual-bandit problems (2012)Bandits With Heavy Tail (2013)KULLBACK-LEIBLER UPPER CONFIDENCE BOUNDS FOR OPTIMAL SEQUENTIAL ALLOCATION (2013)A Stochastic Approximation Method (1951)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Exponentiated Gradient versus Gradient Descent for Linear Predictors (1997)Online convex programming and generalized infinitesimal gradient ascent (2003)Nearly Tight Bounds for the Continuum-Armed Bandit Problem (2004)Linearly Parameterized Bandits (2010)Introduction to Stochastic Programming (2011)The Nonstochastic Multiarmed Bandit Problem (2002)Contextual Bandits with Similarity Information (2009)Almost Optimal Exploration in Multi-Armed Bandits (2013)Prediction, Learning, and Games (2006)Introduction to Online Convex Optimization (2016)Introduction to Online Convex Optimization (2016)Introduction to Multi-Armed Bandits (2019)Applied Statistical Decision Theory (1962)Applied Statistical Decision Theory. (1962)Restless bandits: activity allocation in a changing world (1988)Legislative leviathan: party government in the House (1993)Using confidence bounds for exploitation-exploration trade-offs (2003)Contextual Bandits with Linear Payoff Functions (2011)Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Learning to Optimize via Posterior Sampling (2014)The K-armed dueling bandits problem (2012)Reducing Dueling Bandits to Cardinal Bandits (2014)Contextual Bandits in a Collaborative Environment (2016)Indexability of Restless Bandit Problems and Optimality of Whittle Index for Dynamic Multichannel Access (2010)Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization (2012)Safe Exploration for Optimization with Gaussian Processes (2015)Information-seeking, curiosity, and attention: computational and neural mechanisms (2013)A unified framework for stochastic optimization (2018)Bandit Algorithms (2020)
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References

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  • 2008Stochastic Linear Optimization Under Bandit Feedback581 cited
  • 2008Learning diverse rankings with multi-armed bandits504 cited
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  • 2007Bandits for Taxonomies: A Model-based Approach88 cited
  • 2010Bandit processes and dynamic allocation indices84 cited

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2013
Ranked bandits in metric spaces: learning diverse rankings over large document collections
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