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Model selection for contextual bandits
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Model selection for contextual bandits (2019)Model selection for contextual banditsMostly Exploration-Free Algorithms for Contextual Bandits (2020)Mostly Exploration-Free Algor…A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free (2019)A New Algorithm for Non-stati…Hedging the Drift: Learning to Optimize Under Non-Stationarity (2018)Hedging the Drift: Learning t…Provably Optimal Algorithms for Generalized Linear Contextual Bandits (2017)Provably Optimal Algorithms f…Efficient Contextual Bandits in Non-stationary Worlds (2017)Efficient Contextual Bandits …Explore no more: Improved high-probability regret bounds for non-stochastic bandits (2015)Explore no more: Improved hig…Achieving All with No Parameters: AdaNormalHedge (2015)Achieving All with No Paramet…Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (2014)Taming the Monster: A Fast an…A Second-order Bound with Excess Losses (2014)A Second-order Bound with Exc…Eluder Dimension and the Sample Complexity of Optimistic Exploration (2013)Eluder Dimension and the Samp…Improved Algorithms for Linear Stochastic Bandits (2011)Improved Algorithms for Linea…Contextual Bandits with Linear Payoff Functions (2011)Contextual Bandits with Linea…Contextual Bandit Algorithms with Supervised Learning Guarantees (2010)Contextual Bandit Algorithms …The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information (2007)The Epoch-Greedy Algorithm fo…The Nonstochastic Multiarmed Bandit Problem (2002)The Nonstochastic Multiarmed …A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)A Decision-Theoretic Generali…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Stochastic Linear Optimization Under Bandit Feedback (2008)Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2011)Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Collaborative Filtering Bandits (2016)Some aspects of the sequential design of experiments (1952)Online Learning and Online Convex Optimization (2011)Online Learning and Online Convex Optimization (2012)Using confidence bounds for exploitation-exploration trade-offs (2003)An Empirical Evaluation of Thompson Sampling (2011)Bandits with Knapsacks (2013)Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (2015)Fairness in Learning: Classic and Contextual Bandits (2016)Prediction, Learning, and Games (2006)Efficient Optimal Learning for Contextual Bandits (2011)Doubly Robust Policy Evaluation and Optimization (2014)Doubly Robust Policy Evaluation and Optimization (2014)Gambling in a rigged casino: The adversarial multi-armed bandit problem (2002)Parametric Bandits: The Generalized Linear Case (2010)Online Decision Making with High-Dimensional Covariates (2019)Survey on Applications of Multi-Armed and Contextual Bandits (2020)Bandit Processes and Dynamic Allocation Indices (1979)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Adaptive Treatment Assignment in Experiments for Policy Choice (2021)Meta Dynamic Pricing: Transfer Learning Across Experiments (2021)Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices (2023)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)Learning to Optimize via Posterior Sampling (2014)Introduction to Multi-Armed Bandits (2019)Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles (2020)Hiring as Exploration (2020)Learning Contextual Bandits in a Non-stationary Environment (2018)Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits (2023)
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References

  • 1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting20,469 cited
  • 2002The Nonstochastic Multiarmed Bandit Problem2,210 cited
  • 2011Improved Algorithms for Linear Stochastic Bandits860 cited
  • 2011Contextual Bandits with Linear Payoff Functions599 cited
  • 2007The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information426 cited
  • 2014Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits286 cited
  • 2010Contextual Bandit Algorithms with Supervised Learning Guarantees181 cited
  • 2020Mostly Exploration-Free Algorithms for Contextual Bandits149 cited
  • 2017Provably Optimal Algorithms for Generalized Linear Contextual Bandits141 cited
  • 2017Efficient Contextual Bandits in Non-stationary Worlds100 cited
  • 2018Hedging the Drift: Learning to Optimize Under Non-Stationarity83 cited
  • 2013Eluder Dimension and the Sample Complexity of Optimistic Exploration68 cited
  • 2014A Second-order Bound with Excess Losses67 cited
  • 2019A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free62 cited
  • 2015Explore no more: Improved high-probability regret bounds for non-stochastic bandits

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61 cited
  • 2015Achieving All with No Parameters: AdaNormalHedge54 cited
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