Anastasia Koloskova

According to our database, Anastasia Koloskova authored at least 19 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

On Convergence of Incremental Gradient for Non-convex Smooth Functions.

Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

The Privacy Power of Correlated Noise in Decentralized Learning.

Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization.

Mathieu Even, Anastasia Koloskova, Laurent Massoulié

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders.

Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi

CoRR, 2023

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan

CoRR, 2023

Decentralized Gradient Tracking with Local Steps.

Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich

CoRR, 2023

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees.

Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich

Proceedings of the International Conference on Machine Learning, 2023

2022

Data-heterogeneity-aware Mixing for Decentralized Learning.

Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich

CoRR, 2022

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning.

Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities.

Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning.

Anastasia Koloskova, Tao Lin, Sebastian U. Stich

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Consensus Control for Decentralized Deep Learning.

Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich

Proceedings of the 38th International Conference on Machine Learning, 2021

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates.

Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich

Proceedings of the 37th International Conference on Machine Learning, 2020

Decentralized Deep Learning with Arbitrary Communication Compression.

Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication.

Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Greedy Coordinate Descent for Composite Problems.

Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019