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Using Contextual Bandits with Behavioral Constraints for Constrained Online Movie Recommendation
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Using Contextual Bandits with Behavioral Constraints for Constrained Online Movie Recommendation (2018)Using Contextual Bandits with Behavioral …Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges. (2015)Multi-armed Bandit Models for…Linear Contextual Bandits with Knapsacks (2015)Linear Contextual Bandits wit…Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits (2015)Algorithms with Logarithmic o…Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Thompson Sampling for Context…Contextual Bandits with Linear Payoff Functions (2011)Contextual Bandits with Linea…The Epoch-Greedy algorithm for contextual multi-armed bandits (2007)The Epoch-Greedy algorithm fo…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Choice, Ordering and Morality (1974)Choice, Ordering and MoralityThe 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)The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond (2011)An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives (2015)Incorporating Behavioral Constraints in Online AI Systems (2019)Distributionally Robust Learning (2020)Using confidence bounds for exploitation-exploration trade-offs (2003)An Empirical Evaluation of Thompson Sampling (2011)Further Optimal Regret Bounds for Thompson Sampling (2012)Learning to Optimize via Posterior Sampling (2014)A Tutorial on Thompson Sampling (2018)Gambling in a rigged casino: The adversarial multi-armed bandit problem (2002)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)Linear Stochastic Bandits Under Safety Constraints (2019)Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits Under Realizability (2021)Some aspects of the sequential design of experiments (1952)Bandit Processes and Dynamic Allocation Indices (1979)Simple Bayesian Algorithms for Best-Arm Identification (2020)Whittle index based Q-learning for restless bandits with average reward (2022)Prediction, Learning, and Games (2006)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)A social choice approach to sustainable forest management: an analysis of multiple forest values in Northwestern Ontario (2004)Gamifying collective human behavior with gameful digital rhetoric (2016)
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References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 2012Thompson Sampling for Contextual Bandits with Linear Payoffs600 cited
  • 2011Contextual Bandits with Linear Payoff Functions599 cited
  • 2007The Epoch-Greedy algorithm for contextual multi-armed bandits307 cited
  • 2015Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges.165 cited
  • 2015Linear Contextual Bandits with Knapsacks68 cited
  • 2015Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits61 cited
  • 1974Choice, Ordering and Morality46 cited

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