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An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
14 references·0 citing·35 second hopsampled
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits (2020)An Empirical Process Approach to the Unio…Near-optimal discrete optimization for experimental design: a regret minimization approach (2020)Near-optimal discrete optimiz…Best Arm Identification in Linear Bandits with Linear Dimension Dependency (2018)Best Arm Identification in Li…A fully adaptive algorithm for pure exploration in linear bandits. (2018)A fully adaptive algorithm fo…Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration (2017)Nearly Optimal Sampling Algor…An Efficient Bandit Algorithm for Realtime Multivariate Optimization (2017)An Efficient Bandit Algorithm…Optimal Best Arm Identification with Fixed Confidence (2016)Optimal Best Arm Identificati…An optimal algorithm for the Thresholding Bandit Problem (2016)An optimal algorithm for the …On the Complexity of Best Arm Identification in Multi-Armed Bandit Models (2014)On the Complexity of Best Arm…Combinatorial Pure Exploration of Multi-Armed Bandits (2014)Combinatorial Pure Exploratio…Best-Arm Identification in Linear Bandits (2014)Best-Arm Identification in Li…lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits (2013)lil' UCB : An Optimal Explora…Almost Optimal Exploration in Multi-Armed Bandits (2013)Almost Optimal Exploration in…PAC Subset Selection in Stochastic Multi-armed Bandits (2012)PAC Subset Selection in Stoch…Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems (2006)Action Elimination and Stoppi…Some aspects of the sequential design of experiments (1952)Finite-time Analysis of the Multiarmed Bandit Problem (2002)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (2016)A unified framework for stochastic optimization (2018)Introduction to Multi-Armed Bandits (2019)The Nonstochastic Multiarmed Bandit Problem (2002)Combinatorial Multi-Armed Bandit with General Reward Functions (2016)Non-Asymptotic Pure Exploration by Solving Games (2019)Pure Exploration with Multiple Correct Answers (2019)Optimal design of experiments (1993)Using confidence bounds for exploitation-exploration trade-offs (2003)Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (2018)Fighting Boredom in Recommender Systems with Linear Reinforcement Learning (2018)Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits (2023)Learning diverse rankings with multi-armed bandits (2008)Best Arm Identification in Multi-Armed Bandits (2010)Non-stochastic Best Arm Identification and Hyperparameter Optimization (2015)Gambling in a rigged casino: The adversarial multi-armed bandit problem (2002)The Sample Complexity of Exploration in the Multi-Armed Bandit Problem (2004)Bandit Algorithms (2020)PAC Bounds for Multi-armed Bandit and Markov Decision Processes (2002)Pure Exploration in Multi-armed Bandits Problems (2009)Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-armed Bandits (2019)Good arm identification via bandit feedback (2019)Gamification of Pure Exploration for Linear Bandits (2020)Statistics for experimenters: design, innovation, and discovery (2006)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)A Tutorial on Thompson Sampling (2017)What is the Value of Experimentation and Measurement? (2020)Beyond Preferences in AI Alignment (2024)Improved Algorithms for Linear Stochastic Bandits (2011)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Simple Bayesian Algorithms for Best-Arm Identification (2020)Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence (2012)
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References

  • 2014On the Complexity of Best Arm Identification in Multi-Armed Bandit Models790 cited
  • 2006Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems444 cited
  • 2013Almost Optimal Exploration in Multi-Armed Bandits258 cited
  • 2013lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits196 cited
  • 2012PAC Subset Selection in Stochastic Multi-armed Bandits188 cited
  • 2016Optimal Best Arm Identification with Fixed Confidence115 cited
  • 2017An Efficient Bandit Algorithm for Realtime Multivariate Optimization108 cited
  • 2014Combinatorial Pure Exploration of Multi-Armed Bandits104 cited
  • 2014Best-Arm Identification in Linear Bandits78 cited
  • 2016An optimal algorithm for the Thresholding Bandit Problem68 cited
  • 2020Near-optimal discrete optimization for experimental design: a regret minimization approach48 cited
  • 2018A fully adaptive algorithm for pure exploration in linear bandits.33 cited
  • 2018Best Arm Identification in Linear Bandits with Linear Dimension Dependency30 cited
  • 2017Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration26 cited

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