Anastasia Koloskova

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 30

Top Topics

Correlated Noise

Variational Inequalities

Stochastic Optimization

Top Venues

Publications

Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui
The Privacy Power of Correlated Noise in Decentralized Learning. CoRR (2024)
Mathieu Even, Anastasia Koloskova, Laurent Massoulié
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. AISTATS (2024)
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 (2023)
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 (2023)
Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich
Decentralized Gradient Tracking with Local Steps. CoRR (2023)
Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. CoRR (2023)
Mathieu Even, Anastasia Koloskova, Laurent Massoulié
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. CoRR (2023)
Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. ICML (2023)
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)
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)
Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. NeurIPS (2022)
Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
Data-heterogeneity-aware Mixing for Decentralized Learning. CoRR (2022)
Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. CoRR (2022)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. CoRR (2022)
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)
Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. CoRR (2021)
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 (2021)
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)
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
Consensus Control for Decentralized Deep Learning. CoRR (2021)
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
Consensus Control for Decentralized Deep Learning. ICML (2021)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. NeurIPS (2021)
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)
Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR (2020)
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR (2020)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR (2020)
Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. ICML (2019)
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS (2019)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi
Decentralized Deep Learning with Arbitrary Communication Compression. CoRR (2019)
Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. CoRR (2019)
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Efficient Greedy Coordinate Descent for Composite Problems. CoRR (2018)