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Parametric Bandits: The Generalized Linear Case
7 references·16 citing·33 second hop
Parametric Bandits: The Generalized Linear Case (2010)Parametric Bandits: The Generalized Linea…Linearly Parameterized Bandits (2010)Linearly Parameterized BanditsStochastic Linear Optimization Under Bandit Feedback (2008)Stochastic Linear Optimizatio…The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information (2007)The Epoch-Greedy Algorithm fo…The Epoch-Greedy algorithm for contextual multi-armed bandits (2007)The Epoch-Greedy algorithm fo…Prediction, Learning, and Games (2006)Prediction, Learning, and Gam…Using 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…Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits (2023)Nearly Minimax-Optimal Regret…Mostly Exploration-Free Algorithms for Contextual Bandits (2020)Mostly Exploration-Free Algor…MNL-Bandit: A Dynamic Learning Approach to Assortment Selection (2019)MNL-Bandit: A Dynamic Learnin…Hedging the Drift: Learning to Optimize Under Non-Stationarity (2018)Hedging the Drift: Learning t…Returning is Believing (2017)Returning is BelievingMinimal Exploration in Structured Stochastic Bandits (2017)Minimal Exploration in Struct…Linear Thompson sampling revisited (2017)Linear Thompson sampling revi…Learning to Optimize via Information-Directed Sampling (2017)Learning to Optimize via Info…From Ads to Interventions: Contextual Bandits in Mobile Health (2017)From Ads to Interventions: Co…Learning Hidden Features for Contextual Bandits (2016)Learning Hidden Features for …Contextual Bandits in a Collaborative Environment (2016)Contextual Bandits in a Colla…Cascading Bandits: Learning to Rank in the Cascade Model (2015)Cascading Bandits: Learning t…From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014)From Bandits to Monte-Carlo T…Informational confidence bounds for self-normalized averages and applications (2013)Informational confidence boun…Eluder Dimension and the Sample Complexity of Optimistic Exploration (2013)Eluder Dimension and the Samp…Optimistic bayesian sampling in contextual-bandit problems (2012)Optimistic bayesian sampling …A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Online Learning and Online Convex Optimization (2011)Online Learning and Online Convex Optimization (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Theory of games and economic behavior (2019)Bandit Processes and Dynamic Allocation Indices (1979)Learning diverse rankings with multi-armed bandits (2008)Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations (2012)Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback (2016)Bernoulli Rank-1 Bandits for Click Feedback (2017)A Stochastic Approximation Method (1951)Some aspects of the sequential design of experiments (1952)An Information-Theoretic Analysis of Thompson Sampling (2014)A Tutorial on Thompson Sampling (2018)The Nonstochastic Multiarmed Bandit Problem (2002)Matroid Bandits: Fast Combinatorial Optimization with Learning (2014)Optimal Best Arm Identification with Fixed Confidence (2016)Global optimization of Lipschitz functions (2017)Gambling in a rigged casino: The adversarial multi-armed bandit problem (2002)Multi-armed bandits with episode context (2011)Online Decision Making with High-Dimensional Covariates (2019)Survey on Applications of Multi-Armed and Contextual Bandits (2020)Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems (2006)Interactively optimizing information retrieval systems as a dueling bandits problem (2009)Contextual Gaussian Process Bandit Optimization (2011)Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (2014)Improved Algorithms for Linear Stochastic Bandits (2011)Introduction to Multi-Armed Bandits (2019)Bandit Algorithms (2020)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Contextual Bandits with Linear Payoff Functions (2011)Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Learning to Optimize via Posterior Sampling (2014)
this work references cited by second hop

References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 2006Prediction, Learning, and Games3,176 cited
  • 2003Using confidence bounds for exploitation-exploration trade-offs1,189 cited
  • 2008Stochastic Linear Optimization Under Bandit Feedback581 cited
  • 2010Linearly Parameterized Bandits428 cited
  • 2007The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information426 cited
  • 2007The Epoch-Greedy algorithm for contextual multi-armed bandits307 cited

Cited by

  • 2014From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning233 cited
  • 2012Optimistic bayesian sampling in contextual-bandit problems213 cited
  • 2015Cascading Bandits: Learning to Rank in the Cascade Model169 cited
  • 2017Linear Thompson sampling revisited160 cited
  • 2020Mostly Exploration-Free Algorithms for Contextual Bandits149 cited
  • 2019MNL-Bandit: A Dynamic Learning Approach to Assortment Selection144 cited
  • 2017From Ads to Interventions: Contextual Bandits in Mobile Health130 cited
  • 2017Learning to Optimize via Information-Directed Sampling121 cited

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  • 2017Minimal Exploration in Structured Stochastic Bandits113 cited
  • 2016Contextual Bandits in a Collaborative Environment84 cited
  • 2018Hedging the Drift: Learning to Optimize Under Non-Stationarity83 cited
  • 2017Returning is Believing82 cited
  • 2016Learning Hidden Features for Contextual Bandits79 cited
  • 2023Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits76 cited
  • 2013Informational confidence bounds for self-normalized averages and applications74 cited
  • 2013Eluder Dimension and the Sample Complexity of Optimistic Exploration68 cited