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ORCIDIlyas Fatkhullin
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Journal Articles
- 2021
[j1]Ilyas Fatkhullin
, Boris T. Polyak:
Optimizing Static Linear Feedback: Gradient Method. SIAM J. Control. Optim. 59(5): 3887-3911 (2021)
Conference and Workshop Papers
- 2024
- [c9]Ilyas Fatkhullin, Niao He:
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. AISTATS 2024: 3493-3501 - 2023
- [c8]Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He:
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CDC 2023: 2602-2609 - [c7]Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML 2023: 1753-1800 - [c6]Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML 2023: 9827-9869 - [c5]Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik:
Momentum Provably Improves Error Feedback! NeurIPS 2023 - [c4]Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He:
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. NeurIPS 2023 - 2022
- [c3]Peter Richtárik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov:
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. ICML 2022: 18596-18648 - [c2]Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash:
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. NeurIPS 2022 - 2021
- [c1]Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin:
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. NeurIPS 2021: 4384-4396
Informal and Other Publications
- 2024
- [i10]Ilyas Fatkhullin, Niao He, Yifan Hu:
Stochastic Optimization under Hidden Convexity. CoRR abs/2401.00108 (2024) - [i9]Ilyas Fatkhullin, Niao He:
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. CoRR abs/2402.17722 (2024) - 2023
- [i8]Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. CoRR abs/2302.01734 (2023) - [i7]Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He:
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. CoRR abs/2305.12475 (2023) - [i6]Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik:
Momentum Provably Improves Error Feedback! CoRR abs/2305.15155 (2023) - [i5]Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. CoRR abs/2306.01854 (2023) - [i4]Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He:
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CoRR abs/2309.04272 (2023) - 2022
- [i3]Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard Gorbunov:
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. CoRR abs/2202.00998 (2022) - 2021
- [i2]Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin:
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback. CoRR abs/2106.05203 (2021) - [i1]Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik:
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. CoRR abs/2110.03294 (2021)
Coauthor Index
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