| 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 | 589 | 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 | 585 | 2020 |
| Decentralized deep learning with arbitrary communication compression A Koloskova*, T Lin*, SU Stich, M Jaggi ICLR 2020, 2019 | 270 | 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 | 109 | 2021 |
| Sharper convergence guarantees for asynchronous sgd for distributed and federated learning A Koloskova, SU Stich, M Jaggi NeurIPS 2022, 2022 | 103 | 2022 |
| Consensus control for decentralized deep learning L Kong, T Lin, A Koloskova, M Jaggi, SU Stich ICML 2021, 2021 | 92 | 2021 |
| 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 | 90 | 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 | 69 | 2021 |
| Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees A Koloskova*, H Hendrikx*, SU Stich ICML 2023, 2023 | 55 | 2023 |
| 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 | 50 | 2022 |
| Decentralized gradient tracking with local steps Y Liu, T Lin, A Koloskova, SU Stich Optimization Methods and Software, 1-28, 2024 | 34 | 2024 |
| 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 | 32 | 2019 |
| Data-heterogeneity-aware mixing for decentralized learning Y Dandi, A Koloskova, M Jaggi, SU Stich arXiv preprint arXiv:2204.06477, 2022 | 22 | 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, 35761-35773, 2023 | 18 | 2023 |
| Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization M Even, A Koloskova, L Massoulié AISTATS 2024, 2023 | 14 | 2023 |
| The Privacy Power of Correlated Noise in Decentralized Learning Y Allouah, A Koloskova, AE Firdoussi, M Jaggi, R Guerraoui ICML 2024, 2024 | 8 | 2024 |
| On Convergence of Incremental Gradient for Non-Convex Smooth Functions A Koloskova, N Doikov, SU Stich, M Jaggi ICML 2024, 2023 | 7* | 2023 |
| Decentralized stochastic optimization with client sampling Z Liu, A Koloskova, M Jaggi, T Lin OPT 2022: optimization for machine learning (neurIPS 2022 workshop), 2022 | 6 | 2022 |
| Optimization algorithms for decentralized, distributed and collaborative machine learning A Koloskova EPFL, 2024 | 2 | 2024 |
Sebastian Urban StichCISPA Helmholtz CenterVerified email at cispa.de
