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ORCIDAnastasia Koloskova
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- affiliation: EPFL, Lausanne, Switzerland
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Conference and Workshop Papers
- 2024
[c15]Mathieu Even, Anastasia Koloskova, Laurent Massoulié:
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. AISTATS 2024: 64-72- [c14]Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui:
The Privacy Power of Correlated Noise in Decentralized Learning. ICML 2024 - [c13]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
On Convergence of Incremental Gradient for Non-convex Smooth Functions. ICML 2024 - 2023
- [c12]Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich:
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. ICML 2023: 17343-17363 - [c11]Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan:
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS 2023 - 2022
- [c10]Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov:
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. NeurIPS 2022 - [c9]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. NeurIPS 2022 - 2021
- [c8]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021: 4087-4095 - [c7]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. ICML 2021: 5686-5696 - [c6]Anastasia Koloskova, Tao Lin, Sebastian U. Stich:
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. NeurIPS 2021: 11422-11435 - [c5]Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS 2021: 28004-28015 - 2020
- [c4]Anastasia Koloskova, Tao Lin
, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR 2020 - [c3]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. ICML 2020: 5381-5393 - 2019
- [c2]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS 2019: 2887-2896 - [c1]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. ICML 2019: 3478-3487
Informal and Other Publications
- 2024
- [i17]Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui:
The Privacy Power of Correlated Noise in Decentralized Learning. CoRR abs/2405.01031 (2024) - 2023
- [i16]Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich:
Decentralized Gradient Tracking with Local Steps. CoRR abs/2301.01313 (2023) - [i15]Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan:
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR abs/2302.01463 (2023) - [i14]Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich:
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. CoRR abs/2305.01588 (2023) - [i13]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders. CoRR abs/2305.19259 (2023) - [i12]Mathieu Even, Anastasia Koloskova, Laurent Massoulié:
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. CoRR abs/2311.00465 (2023) - 2022
- [i11]Anastasia Koloskova, Tao Lin, Sebastian U. Stich:
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. CoRR abs/2202.03836 (2022) - [i10]Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Data-heterogeneity-aware Mixing for Decentralized Learning. CoRR abs/2204.06477 (2022) - [i9]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. CoRR abs/2206.08307 (2022) - 2021
- [i8]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. CoRR abs/2102.04828 (2021) - [i7]Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov:
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. CoRR abs/2106.08315 (2021) - [i6]Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2110.04175 (2021) - 2020
- [i5]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR abs/2003.10422 (2020) - [i4]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR abs/2011.01697 (2020) - 2019
- [i3]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. CoRR abs/1902.00340 (2019) - [i2]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. CoRR abs/1907.09356 (2019) - 2018
- [i1]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. CoRR abs/1810.06999 (2018)
Coauthor Index
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