| 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 | 385 | 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 | 290 | 2020 |
| Decentralized deep learning with arbitrary communication compression A Koloskova, T Lin, SU Stich, M Jaggi ICLR 2020, 2019 | 171 | 2019 |
| 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 | 55 | 2021 |
| Consensus control for decentralized deep learning L Kong, T Lin, A Koloskova, M Jaggi, SU Stich ICML 2021, 2021 | 46 | 2021 |
| 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 | 42 | 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 | 32 | 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 | 22 | 2022 |
| 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 | 20 | 2019 |
| Sharper convergence guarantees for asynchronous sgd for distributed and federated learning A Koloskova, SU Stich, M Jaggi NeurIPS 2022, 2022 | 12 | 2022 |
| Data-heterogeneity-aware mixing for decentralized learning Y Dandi, A Koloskova, M Jaggi, SU Stich arXiv preprint arXiv:2204.06477, 2022 | 11 | 2022 |
| Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees A Koloskova, H Hendrikx, SU Stich arXiv preprint arXiv:2305.01588, 2023 | 2 | 2023 |
| Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning A Koloskova, R McKenna, Z Charles, K Rush, B McMahan arXiv preprint arXiv:2302.01463, 2023 | 1 | 2023 |
| Decentralized Gradient Tracking with Local Steps Y Liu, T Lin, A Koloskova, SU Stich arXiv preprint arXiv:2301.01313, 2023 | 1 | 2023 |
| Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders A Koloskova, N Doikov, SU Stich, M Jaggi arXiv preprint arXiv:2305.19259, 2023 | | 2023 |
| Decentralized Stochastic Optimization with Client Sampling Z Liu, A Koloskova, M Jaggi, T Lin OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 0 | | |
Sebastian Urban StichCISPA Helmholtz Center for Information SecurityVerified email at cispa.de
