| 2025 | ICML | Certified Unlearning for Neural Networks. | Anastasia Koloskova, Youssef Allouah, Animesh Jha, Rachid Guerraoui, Sanmi Koyejo |
| 2024 | AISTATS | Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. | Mathieu Even, Anastasia Koloskova, Laurent Massouli |
| 2024 | ICML | The Privacy Power of Correlated Noise in Decentralized Learning. | Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui |
| 2024 | ICML | On Convergence of Incremental Gradient for Non-convex Smooth Functions. | Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi |
| 2023 | ICML | Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. | Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich |
| 2021 | AISTATS | A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! | Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtrik, Sebastian U. Stich |
| 2021 | ICML | Consensus Control for Decentralized Deep Learning. | Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich |
| 2020 | ICLR | Decentralized Deep Learning with Arbitrary Communication Compression. | Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi |
| 2020 | ICML | A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. | Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich |
| 2019 | AISTATS | Efficient Greedy Coordinate Descent for Composite Problems. | Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi |
| 2019 | ICML | Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. | Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi |