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ORCIDJulian Zimmert
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Journal Articles
- 2021
[j1]Julian Zimmert, Yevgeny Seldin:
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits. J. Mach. Learn. Res. 22: 28:1-28:49 (2021)
Conference and Workshop Papers
- 2024
- [c24]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback. ICLR 2024 - 2023
- [c23]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Unified Algorithm for Stochastic Path Problems. ALT 2023: 510-557 - [c22]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. COLT 2023: 5503-5570 - [c21]Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert:
Refined Regret for Adversarial MDPs with Linear Function Approximation. ICML 2023: 6726-6759 - [c20]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. ICML 2023: 6968-7008 - [c19]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits. NeurIPS 2023 - [c18]Jon Schneider, Julian Zimmert:
Optimal cross-learning for contextual bandits with unknown context distributions. NeurIPS 2023 - 2022
- [c17]Naman Agarwal, Satyen Kale, Julian Zimmert:
Efficient Methods for Online Multiclass Logistic Regression. ALT 2022: 3-33 - [c16]Chen-Yu Wei, Christoph Dann, Julian Zimmert:
A Model Selection Approach for Corruption Robust Reinforcement Learning. ALT 2022: 1043-1096 - [c15]Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. COLT 2022: 182-226 - [c14]Julian Zimmert, Tor Lattimore:
Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. COLT 2022: 3285-3312 - [c13]Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. NeurIPS 2022 - [c12]Saeed Masoudian, Julian Zimmert, Yevgeny Seldin:
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback. NeurIPS 2022 - 2021
- [c11]Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. NeurIPS 2021: 1-12 - [c10]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. NeurIPS 2021: 12040-12051 - [c9]Teodor Vanislavov Marinov, Julian Zimmert:
The Pareto Frontier of model selection for general Contextual Bandits. NeurIPS 2021: 17956-17967 - 2020
- [c8]Julian Zimmert, Yevgeny Seldin:
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays. AISTATS 2020: 3285-3294 - [c7]Andrey Kolobov, Sébastien Bubeck, Julian Zimmert:
Online Learning for Active Cache Synchronization. ICML 2020: 5371-5380 - [c6]Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert:
Adapting to Misspecification in Contextual Bandits. NeurIPS 2020 - [c5]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. NeurIPS 2020 - 2019
- [c4]Julian Zimmert, Yevgeny Seldin:
An Optimal Algorithm for Stochastic and Adversarial Bandits. AISTATS 2019: 467-475 - [c3]Julian Zimmert, Haipeng Luo, Chen-Yu Wei:
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. ICML 2019: 7683-7692 - [c2]Julian Zimmert, Tor Lattimore:
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. NeurIPS 2019: 11950-11959 - 2018
- [c1]Julian Zimmert, Yevgeny Seldin:
Factored Bandits. NeurIPS 2018: 2840-2849
Informal and Other Publications
- 2024
- [i26]Jon Schneider, Julian Zimmert:
Optimal cross-learning for contextual bandits with unknown context distributions. CoRR abs/2401.01857 (2024) - [i25]Julian Zimmert, Teodor V. Marinov:
Incentive-compatible Bandits: Importance Weighting No More. CoRR abs/2405.06480 (2024) - 2023
- [i24]Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert:
Refined Regret for Adversarial MDPs with Linear Function Approximation. CoRR abs/2301.12942 (2023) - [i23]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. CoRR abs/2302.09408 (2023) - [i22]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. CoRR abs/2302.09739 (2023) - [i21]Saeed Masoudian, Julian Zimmert, Yevgeny Seldin:
An Improved Best-of-both-worlds Algorithm for Bandits with Delayed Feedback. CoRR abs/2308.10675 (2023) - [i20]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits. CoRR abs/2309.00814 (2023) - [i19]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback. CoRR abs/2310.11550 (2023) - 2022
- [i18]Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. CoRR abs/2202.02765 (2022) - [i17]Teodor V. Marinov, Mehryar Mohri, Julian Zimmert:
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality. CoRR abs/2206.10022 (2022) - [i16]Saeed Masoudian, Julian Zimmert, Yevgeny Seldin:
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback. CoRR abs/2206.14906 (2022) - [i15]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. CoRR abs/2208.10904 (2022) - [i14]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Unified Algorithm for Stochastic Path Problems. CoRR abs/2210.09255 (2022) - 2021
- [i13]Christoph Dann, Teodor V. Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. CoRR abs/2107.01264 (2021) - [i12]Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert:
Adapting to Misspecification in Contextual Bandits. CoRR abs/2107.05745 (2021) - [i11]Naman Agarwal, Satyen Kale, Julian Zimmert:
Efficient Methods for Online Multiclass Logistic Regression. CoRR abs/2110.03020 (2021) - [i10]Chen-Yu Wei, Christoph Dann, Julian Zimmert:
A Model Selection Approach for Corruption Robust Reinforcement Learning. CoRR abs/2110.03580 (2021) - [i9]Teodor V. Marinov, Julian Zimmert:
The Pareto Frontier of model selection for general Contextual Bandits. CoRR abs/2110.13282 (2021) - 2020
- [i8]Andrey Kolobov, Sébastien Bubeck, Julian Zimmert:
Online Learning for Active Cache Synchronization. CoRR abs/2002.12014 (2020) - [i7]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. CoRR abs/2003.01704 (2020) - 2019
- [i6]Julian Zimmert, Haipeng Luo, Chen-Yu Wei:
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. CoRR abs/1901.08779 (2019) - [i5]Julian Zimmert, Tor Lattimore:
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. CoRR abs/1905.11817 (2019) - [i4]Julian Zimmert, Yevgeny Seldin:
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays. CoRR abs/1910.06054 (2019) - 2018
- [i3]Julian Zimmert, Yevgeny Seldin:
Factored Bandits. CoRR abs/1807.01488 (2018) - [i2]Julian Zimmert, Yevgeny Seldin:
An Optimal Algorithm for Stochastic and Adversarial Bandits. CoRR abs/1807.07623 (2018) - 2016
- [i1]Maximilian Alber, Julian Zimmert, Ürün Dogan, Marius Kloft:
Distributed Optimization of Multi-Class SVMs. CoRR abs/1611.08480 (2016)
Coauthor Index
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