| Decentralized stochastic optimization and gossip algorithms with compressed communication A Koloskova*, SU Stich*, M Jaggi ICML 2019 - Proceedings of the 36th International Conference on Machine Learning, 2019 | 584 | 2019 |
| A unified theory of decentralized sgd with changing topology and local updates A Koloskova*, N Loizou, S Boreiri, M Jaggi, SU Stich* ICML 2020, 2020 | 565 | 2020 |
| Decentralized deep learning with arbitrary communication compression A Koloskova*, T Lin*, SU Stich, M Jaggi ICLR 2020, 2019 | 269 | 2019 |
| An improved analysis of gradient tracking for decentralized machine learning A Koloskova, T Lin, SU Stich Advances in Neural Information Processing Systems 34, 11422-11435, 2021 | 108 | 2021 |
| Sharper convergence guarantees for asynchronous sgd for distributed and federated learning A Koloskova, SU Stich, M Jaggi NeurIPS 2022, 2022 | 97 | 2022 |
| A linearly convergent algorithm for decentralized optimization: Sending less bits for free! D Kovalev, A Koloskova, M Jaggi, P Richtarik, S Stich International Conference on Artificial Intelligence and Statistics, 4087-4095, 2021 | 91 | 2021 |
| Consensus control for decentralized deep learning L Kong, T Lin, A Koloskova, M Jaggi, SU Stich ICML 2021, 2021 | 89 | 2021 |
| Relaysum for decentralized deep learning on heterogeneous data T Vogels*, L He*, A Koloskova, SP Karimireddy, T Lin, SU Stich, M Jaggi Advances in Neural Information Processing Systems 34, 28004-28015, 2021 | 70 | 2021 |
| Decentralized local stochastic extra-gradient for variational inequalities A Beznosikov, P Dvurechenskii, A Koloskova, V Samokhin, SU Stich, ... Advances in Neural Information Processing Systems 35, 38116-38133, 2022 | 48 | 2022 |
| Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees A Koloskova*, H Hendrikx*, SU Stich ICML 2023, 2023 | 43 | 2023 |
| Efficient greedy coordinate descent for composite problems SP Karimireddy*, A Koloskova*, SU Stich, M Jaggi The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 33 | 2019 |
| Decentralized gradient tracking with local steps Y Liu, T Lin, A Koloskova, SU Stich Optimization Methods and Software, 1-28, 2024 | 30 | 2024 |
| Data-heterogeneity-aware mixing for decentralized learning Y Dandi, A Koloskova, M Jaggi, SU Stich arXiv preprint arXiv:2204.06477, 2022 | 21 | 2022 |
| Gradient descent with linearly correlated noise: Theory and applications to differential privacy A Koloskova, R McKenna, Z Charles, J Rush, HB McMahan Advances in Neural Information Processing Systems 36, 2023 | 17 | 2023 |
| Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization M Even, A Koloskova, L Massoulié AISTATS 2024, 2023 | 13 | 2023 |
| On Convergence of Incremental Gradient for Non-Convex Smooth Functions A Koloskova, N Doikov, SU Stich, M Jaggi ICML 2024, 2023 | 7* | 2023 |
| The Privacy Power of Correlated Noise in Decentralized Learning Y Allouah, A Koloskova, AE Firdoussi, M Jaggi, R Guerraoui ICML 2024, 2024 | 5 | 2024 |
| Decentralized stochastic optimization with client sampling Z Liu, A Koloskova, M Jaggi, T Lin OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022 | 5 | 2022 |
| Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning A Koloskova EPFL, 2024 | 2 | 2024 |
Sebastian Urban StichCISPA Helmholtz CenterEmail verificata su cispa.de