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ORCIDNikita Zhivotovskiy
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2020 – today
- 2024
[c14]Ishaq Aden-Ali, Mikael Møller Høandgsgaard, Kasper Green Larsen, Nikita Zhivotovskiy:
Majority-of-Three: The Simplest Optimal Learner? COLT 2024: 22-45- [i24]Ishaq Aden-Ali, Mikael Møller Høgsgaard, Kasper Green Larsen, Nikita Zhivotovskiy:
Majority-of-Three: The Simplest Optimal Learner? CoRR abs/2403.08831 (2024) - [i23]Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy:
Revisiting Agnostic PAC Learning. CoRR abs/2407.19777 (2024) - 2023
- [c13]Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy:
The One-Inclusion Graph Algorithm is not Always Optimal. COLT 2023: 72-88 - [c12]Nikita Puchkin, Nikita Zhivotovskiy:
Exploring Local Norms in Exp-concave Statistical Learning. COLT 2023: 1993-2013 - [c11]Jaouad Mourtada, Tomas Vaskevicius, Nikita Zhivotovskiy:
Local Risk Bounds for Statistical Aggregation. COLT 2023: 5697-5698 - [c10]Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy:
Optimal PAC Bounds without Uniform Convergence. FOCS 2023: 1203-1223 - [i22]Arshak Minasyan, Nikita Zhivotovskiy:
Statistically Optimal Robust Mean and Covariance Estimation for Anisotropic Gaussians. CoRR abs/2301.09024 (2023) - [i21]Nikita Puchkin, Nikita Zhivotovskiy:
Exploring Local Norms in Exp-concave Statistical Learning. CoRR abs/2302.10726 (2023) - [i20]Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy:
Optimal PAC Bounds Without Uniform Convergence. CoRR abs/2304.09167 (2023) - [i19]Jaouad Mourtada, Tomas Vaskevicius, Nikita Zhivotovskiy:
Local Risk Bounds for Statistical Aggregation. CoRR abs/2306.17151 (2023) - [i18]Dirk van der Hoeven, Nikita Zhivotovskiy, Nicolò Cesa-Bianchi:
High-Probability Risk Bounds via Sequential Predictors. CoRR abs/2308.07588 (2023) - 2022
- [j3]Nikita Puchkin
, Nikita Zhivotovskiy:
Exponential Savings in Agnostic Active Learning Through Abstention. IEEE Trans. Inf. Theory 68(7): 4651-4665 (2022) - [c9]Dirk van der Hoeven, Nikita Zhivotovskiy, Nicolò Cesa-Bianchi:
A Regret-Variance Trade-Off in Online Learning. NeurIPS 2022 - [i17]Pedro Abdalla, Nikita Zhivotovskiy:
Covariance Estimation: Optimal Dimension-free Guarantees for Adversarial Corruption and Heavy Tails. CoRR abs/2205.08494 (2022) - [i16]Dirk van der Hoeven, Nikita Zhivotovskiy, Nicolò Cesa-Bianchi:
A Regret-Variance Trade-Off in Online Learning. CoRR abs/2206.02656 (2022) - [i15]Afonso S. Bandeira, Antoine Maillard, Nikita Zhivotovskiy:
A remark on Kashin's discrepancy argument and partial coloring in the Komlós conjecture. CoRR abs/2206.08734 (2022) - [i14]Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy:
The One-Inclusion Graph Algorithm is not Always Optimal. CoRR abs/2212.09270 (2022) - [i13]Wolfgang Karl Härdle, Yegor Klochkov, Alla Petukhina, Nikita Zhivotovskiy:
Robustifying Markowitz. CoRR abs/2212.13996 (2022) - 2021
- [c8]Nikita Puchkin, Nikita Zhivotovskiy:
Exponential savings in agnostic active learning through abstention. COLT 2021: 3806-3832 - [c7]Yegor Klochkov, Nikita Zhivotovskiy:
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$. NeurIPS 2021: 5065-5076 - [i12]Nikita Puchkin, Nikita Zhivotovskiy:
Exponential Savings in Agnostic Active Learning through Abstention. CoRR abs/2102.00451 (2021) - [i11]Jaouad Mourtada, Tomas Vaskevicius, Nikita Zhivotovskiy:
Distribution-Free Robust Linear Regression. CoRR abs/2102.12919 (2021) - [i10]Yegor Klochkov, Nikita Zhivotovskiy:
Stability and Deviation Optimal Risk Bounds with Convergence Rate O(1/n). CoRR abs/2103.12024 (2021) - 2020
- [j2]Andrey Kupavskii, Nikita Zhivotovskiy:
When are epsilon-nets small? J. Comput. Syst. Sci. 110: 22-36 (2020) - [c6]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. COLT 2020: 582-609 - [c5]Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy:
Sharper Bounds for Uniformly Stable Algorithms. COLT 2020: 610-626 - [c4]Gergely Neu, Nikita Zhivotovskiy:
Fast Rates for Online Prediction with Abstention. COLT 2020: 3030-3048 - [i9]Gergely Neu, Nikita Zhivotovskiy:
Fast Rates for Online Prediction with Abstention. CoRR abs/2001.10623 (2020) - [i8]Yegor Klochkov, Alexey Kroshnin, Nikita Zhivotovskiy:
Robust k-means Clustering for Distributions with Two Moments. CoRR abs/2002.02339 (2020) - [i7]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR abs/2005.11818 (2020) - [i6]Tomas Vaskevicius, Nikita Zhivotovskiy:
Suboptimality of Constrained Least Squares and Improvements via Non-Linear Predictors. CoRR abs/2009.09304 (2020) - [i5]Luc Devroye, Silvio Lattanzi, Gábor Lugosi, Nikita Zhivotovskiy:
On Mean Estimation for Heteroscedastic Random Variables. CoRR abs/2010.11537 (2020)
2010 – 2019
- 2019
- [i4]Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy:
Sharper bounds for uniformly stable algorithms. CoRR abs/1910.07833 (2019) - [i3]Olivier Bousquet, Nikita Zhivotovskiy:
Fast classification rates without standard margin assumptions. CoRR abs/1910.12756 (2019) - 2018
- [j1]Nikita Zhivotovskiy, Steve Hanneke:
Localization of VC classes: Beyond local Rademacher complexities. Theor. Comput. Sci. 742: 27-49 (2018) - 2017
- [c3]Nikita Zhivotovskiy:
Optimal learning via local entropies and sample compression. COLT 2017: 2023-2065 - [i2]Andrey Kupavskii, Nikita Zhivotovskiy:
When are epsilon-nets small? CoRR abs/1711.10414 (2017) - 2016
- [c2]Nikita Zhivotovskiy, Steve Hanneke:
Localization of VC Classes: Beyond Local Rademacher Complexities. ALT 2016: 18-33 - 2015
- [c1]Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard:
Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning. ALT 2015: 209-223 - [i1]Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard:
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning. CoRR abs/1505.02910 (2015)
Coauthor Index
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last updated on 2024-10-07 21:12 CEST by the dblp team
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