On Convergence of Incremental Gradient for Non-Convex Smooth Functions Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi ICML 2024 • Paper • BibTex
The Privacy Power of Correlated Noise in Decentralized Learning Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui ICML 2024 • Paper • BibTex
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization Mathieu Even, Anastasia Koloskova, Laurent Massoulié AISTATS 2024 • Paper • BibTex
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan NeurIPS 2023 • Paper • BibTex
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees Anastasia Koloskova*, Hadrien Hendrikx*, Sebastian U Stich ICML 2023 • Paper • BibTex
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasia Koloskova, Sebastian U Stich, Martin Jaggi NeurIPS 2022 (Oral, Notable top 7%) • Paper • BibTex
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities Aleksandr Beznosikov, Pavel Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander Gasnikov NeurIPS 2022 • Paper • BibTex
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning Anastasia Koloskova, Tao Lin, Sebastian U Stich NeurIPS 2021 • Paper • BibTex
RelaySum for Decentralized Deep Learning on Heterogeneous Data Thijs Vogels*, Lie He*, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U Stich, Martin Jaggi NeurIPS 2021 • Paper • Code • BibTex
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich AISTATS 2021 • Paper • BibTex
Consensus Control for Decentralized Deep Learning Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich ICML 2021 • Paper • BibTex
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates Anastasia Koloskova*, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich* ICML 2020 • Paper • BibTex
Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova*, Tao Lin*, Sebastian U. Stich, Martin Jaggi ICLR 2020 • Paper • BibTex
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication Anastasia Koloskova*, Sebastian U. Stich*, Martin Jaggi ICML 2019 • Paper • BibTex
Efficient Greedy Coordinate Descent for Composite Problems Sai Praneeth Karimireddy*, Anastasia Koloskova*, Sebastian U. Stich, Martin Jaggi AISTATS 2019 • Paper • BibTex
Selected Talks and Presentations
Methodological Aspects of Federated Learning, Challenges and Opportunities Basel Biometric Society, 2023 • link
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning Simons Berkeley Federated & Collaborative Learning Workshop, 2023 • link
Sharper Convergence Guarantees for Asynchronous SGD Federated Learning One World Seminar, 2022 • link
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates Federated Learning One World Seminar, 2021 • link
Choco-SGD: Communication Efficient Decentralized Learning Applied Machine Learning Days, 2020 • link
Communication Efficient Decentralized Machine Learning Youtube video @ ZettaBytes, EPFL, 2019 • link
Communication Efficient Decentralized Machine Learning IC (CS) Department Research Day, EPFL, 2019 • link
