Zimmert, Julian

1. Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
   2018/07/19 by Zimmert, Julian, Seldin, Yevgeny · 12 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
2. Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
   2019/01/25 by Julian Zimmert, Haipeng Luo, Zimmert, Julian +3 · 7 citations
   Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Optimization and Search Problems
3. Adapting to Misspecification in Contextual Bandits
   2021/07/12 by Dylan J. Foster, Foster, Dylan J., Claudio Gentile +5 · 10 citations
   Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
4. An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays
   2019/10/14 by Julian Zimmert, Yevgeny Seldin, Zimmert, Julian +1 · 6 citations
   Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Machine Learning and Algorithms #Stochastic Gradient Optimization Techniques
5. Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds\n for Episodic Reinforcement Learning
   2021/07/02 by Christoph Dann, Dann, Christoph, Teodor V. Marinov +5 · 5 citations
   Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Smart Grid Energy Management
6. A Model Selection Approach for Corruption Robust Reinforcement Learning
   2021/10/07 by Wei, Chen-Yu, Dann, Christoph, Zimmert, Julian · 4 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
7. Model Selection in Contextual Stochastic Bandit Problems
   2020/03/03 by Aldo Pacchiano, My V. T. Phan, Pacchiano, Aldo +11 · 8 citations
   Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Reinforcement Learning in Robotics
8. A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
   2023/02/20 by Dann, Christoph, Wei, Chen-Yu, Zimmert, Julian · 4 citations
   #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
9. A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
   2023/08/21 by Masoudian, Saeed, Zimmert, Julian, Seldin, Yevgeny · 3 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
10. The Pareto Frontier of model selection for general Contextual Bandits
    2021/10/25 by Teodor V. Marinov, Marinov, Teodor V., Julian Zimmert +1 · 2 citations
    Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Machine Learning and Algorithms #Reinforcement Learning in Robotics
11. Best of Both Worlds Policy Optimization
    2023/02/18 by Christoph Dann, Chen-Yu Wei, Dann, Christoph +3 · 2 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics
12. Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
    2023/09/02 by Liu, Haolin, Wei, Chen-Yu, Zimmert, Julian · 2 citations
    #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
13. Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States
    2022/02/06 by Zimmert, Julian, Agarwal, Naman, Kale, Satyen · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
14. A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
    2022/06/29 by Masoudian, Saeed, Zimmert, Julian, Seldin, Yevgeny · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
15. Refined Regret for Adversarial MDPs with Linear Function Approximation
    2023/01/30 by Yan Dai, Haipeng Luo, Dai, Yan +5 · 2 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
16. Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
    2019/05/28 by Julian Zimmert, Zimmert, Julian, Tor Lattimore +1 · 1 citation
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics
17. Optimal cross-learning for contextual bandits with unknown context distributions
    2024/01/03 by Jon Schneider, Schneider, Jon, Julian Zimmert +1 · 1 citation
    Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Machine Learning and Algorithms #Auction Theory and Applications
18. Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
    2023/10/17 by Haolin Liu, Liu, Haolin, Chen-Yu Wei +3 · 2 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics