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Spectral Thompson Sampling
14 references·1 citing·16 second hop
Spectral Thompson Sampling (2014)Spectral Thompson SamplingLearning to Optimize via Posterior Sampling (2014)Learning to Optimize via Post…Thompson Sampling for 1-Dimensional Exponential Family Bandits (2013)Thompson Sampling for 1-Dimen…Eluder Dimension and the Sample Complexity of Optimistic Exploration (2013)Eluder Dimension and the Samp…Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis (2012)Thompson Sampling: An Asympto…Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Thompson Sampling for Context…Optimistic bayesian sampling in contextual-bandit problems (2012)Optimistic bayesian sampling …Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Information-Theoretic Regret …Further Optimal Regret Bounds for Thompson Sampling (2012)Further Optimal Regret Bounds…Improved Algorithms for Linear Stochastic Bandits (2011)Improved Algorithms for Linea…Analysis of Thompson Sampling for the multi-armed bandit problem (2011)Analysis of Thompson Sampling…An Empirical Evaluation of Thompson Sampling (2011)An Empirical Evaluation of Th…The Price of Bandit Information for Online Optimization (2007)The Price of Bandit Informati…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…Neural Thompson Sampling (2020)Neural Thompson SamplingMulti-Armed Bandit Allocation Indices. (1990)Introduction to Multi-Armed Bandits (2019)Some aspects of the sequential design of experiments (1952)Bandit Algorithms (2020)Prediction, Learning, and Games (2006)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)An Information-Theoretic Analysis of Thompson Sampling (2014)From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014)Linear Thompson sampling revisited (2017)The Nonstochastic Multiarmed Bandit Problem (2002)A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010)A Tutorial on Thompson Sampling (2018)A unified framework for stochastic optimization (2018)Online Decision Making with High-Dimensional Covariates (2019)KULLBACK-LEIBLER UPPER CONFIDENCE BOUNDS FOR OPTIMAL SEQUENTIAL ALLOCATION (2013)Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments (2017)
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References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 2012Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting2,098 cited
  • 2003Using confidence bounds for exploitation-exploration trade-offs1,189 cited
  • 2011An Empirical Evaluation of Thompson Sampling1,000 cited
  • 2011Analysis of Thompson Sampling for the multi-armed bandit problem904 cited
  • 2011Improved Algorithms for Linear Stochastic Bandits860 cited
  • 2012Thompson Sampling for Contextual Bandits with Linear Payoffs600 cited
  • 2014Learning to Optimize via Posterior Sampling494 cited
  • 2012Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis381 cited
  • 2012Further Optimal Regret Bounds for Thompson Sampling308 cited
  • 2012Optimistic bayesian sampling in contextual-bandit problems213 cited
  • 2007The Price of Bandit Information for Online Optimization161 cited
  • 2013Thompson Sampling for 1-Dimensional Exponential Family Bandits88 cited
  • 2013Eluder Dimension and the Sample Complexity of Optimistic Exploration68 cited

Cited by

  • 2020Neural Thompson Sampling40 cited

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