| 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 | 524 | 2020 |
| Distributed learning with compressed gradient differences K Mishchenko, E Gorbunov, M Takáč, P Richtárik Optimization Methods and Software, 1-16, 2019 | 242* | 2019 |
| Stochastic distributed learning with gradient quantization and double-variance reduction S Horváth, D Kovalev, K Mishchenko, P Richtárik, S Stich Optimization Methods and Software, 1-16, 2022 | 200 | 2022 |
| First Analysis of Local GD on Heterogeneous Data A Khaled, K Mishchenko, P Richtárik NeurIPS FL Workshop, arXiv preprint arXiv:1909.04715, 2019 | 192 | 2019 |
| ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! K Mishchenko, G Malinovsky, S Stich, P Richtárik International Conference on Machine Learning, 15750-15769, 2022 | 169 | 2022 |
| Random Reshuffling: Simple Analysis with Vast Improvements K Mishchenko, A Khaled, P Richtárik Advances in Neural Information Processing Systems 33, 17309-17320, 2020 | 160 | 2020 |
| Adaptive gradient descent without descent Y Malitsky, K Mishchenko International Conference on Machine Learning, 6702-6712, 2020 | 121 | 2020 |
| Revisiting stochastic extragradient K Mishchenko, D Kovalev, E Shulgin, P Richtárik, Y Malitsky International Conference on Artificial Intelligence and Statistics, 4573-4582, 2020 | 95 | 2020 |
| SEGA: Variance Reduction via Gradient Sketching F Hanzely, K Mishchenko, P Richtárik Advances in Neural Information Processing Systems, 2082-2093, 2018 | 92 | 2018 |
| Learning-Rate-Free Learning by D-Adaptation A Defazio, K Mishchenko International Conference on Machine Learning, 2023 | 81 | 2023 |
| Asynchronous SGD Beats Minibatch SGD under Arbitrary Delays K Mishchenko, F Bach, M Even, B Woodworth Advances in Neural Information Processing Systems 35, 420-433, 2022 | 58 | 2022 |
| Regularized Newton Method with Global Convergence K Mishchenko SIAM Journal on Optimization 33 (3), 1440-1462, 2023 | 52 | 2023 |
| Stochastic Newton and cubic Newton methods with simple local linear-quadratic rates D Kovalev, K Mishchenko, P Richtárik NeurIPS Workshop Beyond First Order Methods in ML, arXiv preprint arXiv:1912 …, 2019 | 50 | 2019 |
| 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 | 49 | 2018 |
| Proximal and Federated Random Reshuffling K Mishchenko, A Khaled, P Richtárik International Conference on Machine Learning, 15718-15749, 2022 | 47 | 2022 |
| Dualize, split, randomize: Toward fast nonsmooth optimization algorithms A Salim, L Condat, K Mishchenko, P Richtárik Journal of Optimization Theory and Applications 195 (1), 102-130, 2022 | 44 | 2022 |
| Prodigy: An expeditiously adaptive parameter-free learner K Mishchenko, A Defazio International Conference on Machine Learning, 2024 | 38 | 2024 |
| 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 | 35* | 2020 |
| 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 | 33 | 2019 |
| IntSGD: Adaptive Floatless Compression of Stochastic Gradients K Mishchenko, B Wang, D Kovalev, P Richtárik ICLR 2022 - International Conference on Learning Representations, 2022 | 31* | 2022 |
Peter RichtarikProfessor, KAUST在 kaust.edu.sa 的电子邮件经过验证