Efficient Greedy Coordinate Descent for Composite Problems. - Sai Praneeth Karimireddy; Anastasia Koloskova; Sebastian U. Stich; Martin Jaggi (AISTATS, 2019)

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. - Anastasia Koloskova; Sebastian U. Stich; Martin Jaggi (ICML, 2019)

Decentralized Deep Learning with Arbitrary Communication Compression. - Anastasia Koloskova; Tao Lin 004; Sebastian U. Stich; Martin Jaggi (ICLR, 2020)

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)

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)

Consensus Control for Decentralized Deep Learning. - Lingjing Kong 001; Tao Lin 004; Anastasia Koloskova; Martin Jaggi; Sebastian U. Stich (ICML, 2021)

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. - Anastasia Koloskova; Tao Lin 004; Sebastian U. Stich (NeurIPS, 2021)

RelaySum for Decentralized Deep Learning on Heterogeneous Data. - Thijs Vogels; Lie He; Anastasia Koloskova; Sai Praneeth Karimireddy; Tao Lin 004; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2021)

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. - Aleksandr Beznosikov; Pavel E. Dvurechensky; Anastasia Koloskova; Valentin Samokhin; Sebastian U. Stich; Alexander V. Gasnikov (NeurIPS, 2022)

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. - Anastasia Koloskova; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2022)

Data-heterogeneity-aware Mixing for Decentralized Learning. - Yatin Dandi; Anastasia Koloskova; Martin Jaggi; Sebastian U. Stich (CoRR, 2022)

Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. - Anastasia Koloskova; Hadrien Hendrikx; Sebastian U. Stich (ICML, 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 (NeurIPS, 2023)

Decentralized Gradient Tracking with Local Steps. - Yue Liu; Tao Lin 004; Anastasia Koloskova; Sebastian U. Stich (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)

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)

Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. - Mathieu Even; Anastasia Koloskova; Laurent Massoulié (AISTATS, 2024)

The Privacy Power of Correlated Noise in Decentralized Learning. - Youssef Allouah; Anastasia Koloskova; Aymane El Firdoussi; Martin Jaggi; Rachid Guerraoui (ICML, 2024)

On Convergence of Incremental Gradient for Non-convex Smooth Functions. - Anastasia Koloskova; Nikita Doikov; Sebastian U. Stich; Martin Jaggi (ICML, 2024)

Certified Unlearning for Neural Networks. - Anastasia Koloskova; Youssef Allouah; Animesh Jha; Rachid Guerraoui; Sanmi Koyejo (CoRR, 2025)