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Linear Submodular Bandits and their Application to Diversified Retrieval
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Linear Submodular Bandits and their Application to Diversified Retrieval (2011)Linear Submodular Bandits and their Appli…Contextual Bandits with Linear Payoff Functions (2011)Contextual Bandits with Linea…Linearly Parameterized Bandits (2010)Linearly Parameterized BanditsStochastic Linear Optimization Under Bandit Feedback (2008)Stochastic Linear Optimizatio…Learning diverse rankings with multi-armed bandits (2008)Learning diverse rankings wit…Competing in the dark: An efficient algorithm for bandit linear optimization (2008)Competing in the dark: An eff…The Epoch-Greedy algorithm for contextual multi-armed bandits (2007)The Epoch-Greedy algorithm fo…Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits (2023)Nearly Minimax-Optimal Regret…An Efficient Simulation-Based Approach to Ambulance Fleet Allocation and Dynamic Redeployment (2021)An Efficient Simulation-Based…Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization (2016)Noisy Submodular Maximization…Learning Hidden Features for Contextual Bandits (2016)Learning Hidden Features for …Matroid Bandits: Fast Combinatorial Optimization with Learning (2014)Matroid Bandits: Fast Combina…Efficient Learning in Large-Scale Combinatorial Semi-Bandits (2014)Efficient Learning in Large-S…Adaptive Submodular Maximization in Bandit Setting (2013)Adaptive Submodular Maximizat…A fast bandit algorithm for recommendations to users with heterogeneous tastes (2013)A fast bandit algorithm for r…A Fast Bandit Algorithm for Recommendation to Users With Heterogenous Tastes (2013)A Fast Bandit Algorithm for R…Contextual Sequence Prediction with Application to Control Library Optimization (2012)Contextual Sequence Predictio…Finite-time Analysis of the Multiarmed Bandit Problem (2002)The Nonstochastic Multiarmed Bandit Problem (2002)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)Prediction, Learning, and Games (2006)Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints (2011)Online Learning with Predictable Sequences (2012)The best of both worlds: stochastic and adversarial bandits (2012)A Stochastic Approximation Method (1951)A Tutorial on Thompson Sampling (2018)Online Decision Making with High-Dimensional Covariates (2019)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)Contextual Gaussian Process Bandit Optimization (2011)Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (2014)Using confidence bounds for exploitation-exploration trade-offs (2003)An Empirical Evaluation of Thompson Sampling (2011)Transferable Contextual Bandit for Cross-Domain Recommendation (2018)Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests (2020)Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback (2016)Stochastic Online Shortest Path Routing: The Value of Feedback (2017)Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (2018)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles (2020)A Simple Approach for Non-stationary Linear Bandits (2021)Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits Under Realizability (2021)Monte Carlo bounding techniques for determining solution quality in stochastic programs (1999)Ambulance location and relocation models (2003)Approximate Dynamic Programming for Ambulance Redeployment (2009)A nested-compliance table policy for emergency medical service systems under relocation (2015)Dynamic Distribution of Patients to Medical Facilities in the Aftermath of a Disaster (2018)A Deep Reinforcement Learning-Enabled Dynamic Redeployment System for Mobile Ambulances (2019)Contextual Bandits with Similarity Information (2009)Interactively optimizing information retrieval systems as a dueling bandits problem (2009)Almost Optimal Exploration in Multi-Armed Bandits (2013)Learning to Optimize via Posterior Sampling (2014)
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References

  • 2011Contextual Bandits with Linear Payoff Functions599 cited
  • 2008Stochastic Linear Optimization Under Bandit Feedback581 cited
  • 2008Learning diverse rankings with multi-armed bandits504 cited
  • 2010Linearly Parameterized Bandits428 cited
  • 2007The Epoch-Greedy algorithm for contextual multi-armed bandits307 cited
  • 2008Competing in the dark: An efficient algorithm for bandit linear optimization182 cited

Cited by

  • 2014Matroid Bandits: Fast Combinatorial Optimization with Learning80 cited
  • 2016Learning Hidden Features for Contextual Bandits79 cited
  • 2023Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits76 cited
  • 2021An Efficient Simulation-Based Approach to Ambulance Fleet Allocation and Dynamic Redeployment70 cited
  • 2014Efficient Learning in Large-Scale Combinatorial Semi-Bandits48 cited
  • 2013A fast bandit algorithm for recommendations to users with heterogeneous tastes47 cited
  • 2016Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization44 cited
  • 2012Contextual Sequence Prediction with Application to Control Library Optimization36 cited
  • 2013Adaptive Submodular Maximization in Bandit Setting

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35 cited
  • 2013A Fast Bandit Algorithm for Recommendation to Users With Heterogenous Tastes31 cited