| Tighter Theory for Local SGD on Identical and Heterogeneous Data A Khaled, K Mishchenko, P Richtarik International Conference on Artificial Intelligence and Statistics, 4519-4529, 2020 | 65* | 2020 |
| Stochastic Distributed Learning with Gradient Quantization and Variance Reduction S Horváth, D Kovalev, K Mishchenko, S Stich, P Richtárik arXiv preprint arXiv:1904.05115, 2019 | 50 | 2019 |
| Distributed Learning with Compressed Gradient Differences K Mishchenko, E Gorbunov, M Takáč, P Richtárik arXiv preprint arXiv:1901.09269, 2019 | 45 | 2019 |
| First Analysis of Local GD on Heterogeneous Data A Khaled, K Mishchenko, P Richtárik NeurIPS FL Workshop, arXiv preprint arXiv:1909.04715, 2019 | 38 | 2019 |
| SEGA: Variance Reduction via Gradient Sketching F Hanzely, K Mishchenko, P Richtárik Advances in Neural Information Processing Systems, 2082-2093, 2018 | 32 | 2018 |
| A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning K Mishchenko, F Iutzeler, J Malick, MR Amini International Conference on Machine Learning, 3584-3592, 2018 | 21 | 2018 |
| Revisiting Stochastic Extragradient K Mishchenko, D Kovalev, E Shulgin, P Richtárik, Y Malitsky AISTATS 2020, 2019 | 17 | 2019 |
| 99% of worker-master communication in distributed optimization is not needed K Mishchenko, F Hanzely, P Richtárik Conference on Uncertainty in Artificial Intelligence, 979-988, 2020 | 16* | 2020 |
| A distributed flexible delay-tolerant proximal gradient algorithm K Mishchenko, F Iutzeler, J Malick SIAM Journal on Optimization 30 (1), 933-959, 2020 | 15 | 2020 |
| Stochastic Newton and cubic Newton methods with simple local linear-quadratic rates D Kovalev, K Mishchenko, P Richtárik arXiv preprint arXiv:1912.01597, 2019 | 12 | 2019 |
| Adaptive Gradient Descent without Descent K Mishchenko, Y Malitsky 37th International Conference on Machine Learning (ICML 2020), 2020 | 10* | 2020 |
| MISO is Making a Comeback With Better Proofs and Rates X Qian, A Sailanbayev, K Mishchenko, P Richtárik arXiv preprint arXiv:1906.01474, 2019 | 8 | 2019 |
| A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions K Mishchenko, P Richtárik arXiv preprint arXiv:1905.11535, 2019 | 8 | 2019 |
| DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate S Soori, K Mischenko, A Mokhtari, MM Dehnavi, M Gurbuzbalaban AISTATS 2020, 2019 | 7 | 2019 |
| Random Reshuffling: Simple Analysis with Vast Improvements K Mishchenko, A Khaled, P Richtárik Advances in Neural Information Processing Systems 33, 2020 | 6 | 2020 |
| A Stochastic Penalty Model for Convex and Nonconvex Optimization with Big Constraints K Mishchenko, P Richtárik arXiv preprint arXiv:1810.13387, 2018 | 4 | 2018 |
| Dualize, split, randomize: Fast nonsmooth optimization algorithms A Salim, L Condat, K Mishchenko, P Richtárik arXiv preprint arXiv:2004.02635, 2020 | 3 | 2020 |
| Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent K Mishchenko NeurIPS OTML Workshop, arXiv preprint arXiv:1909.06918, 2019 | 3 | 2019 |
| A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls K Mishchenko, M Montgomery, F Vaggi Time Series Workshop at International Conference on Machine Learning, 2019 | 1 | 2019 |
| IntSGD: Floatless Compression of Stochastic Gradients K Mishchenko, B Wang, D Kovalev, P Richtárik arXiv preprint arXiv:2102.08374, 2021 | | 2021 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
