| 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 | 66 | 2019 |
| Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop D Kovalev, S Horváth, P Richtárik Algorithmic Learning Theory, 451-467, 2020 | 59 | 2020 |
| Acceleration for compressed gradient descent in distributed and federated optimization Z Li, D Kovalev, X Qian, P Richtárik arXiv preprint arXiv:2002.11364, 2020 | 40 | 2020 |
| From Local SGD to Local Fixed-Point Methods for Federated Learning G Malinovskiy, D Kovalev, E Gasanov, L Condat, P Richtarik International Conference on Machine Learning, 6692-6701, 2020 | 27 | 2020 |
| Revisiting stochastic extragradient K Mishchenko, D Kovalev, E Shulgin, P Richtárik, Y Malitsky International Conference on Artificial Intelligence and Statistics, 4573-4582, 2020 | 22 | 2020 |
| Rsn: Randomized subspace newton RM Gower, D Kovalev, F Lieder, P Richtárik arXiv preprint arXiv:1905.10874, 2019 | 21 | 2019 |
| Stochastic Newton and cubic Newton methods with simple local linear-quadratic rates D Kovalev, K Mishchenko, P Richtárik arXiv preprint arXiv:1912.01597, 2019 | 18 | 2019 |
| Optimal and practical algorithms for smooth and strongly convex decentralized optimization D Kovalev, A Salim, P Richtárik Advances in Neural Information Processing Systems 33, 2020 | 12 | 2020 |
| Accelerated methods for composite non-bilinear saddle point problem M Alkousa, D Dvinskikh, F Stonyakin, A Gasnikov, D Kovalev arXiv preprint arXiv:1906.03620, 2019 | 12 | 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 | 11 | 2021 |
| Linearly converging error compensated SGD E Gorbunov, D Kovalev, D Makarenko, P Richtárik arXiv preprint arXiv:2010.12292, 2020 | 11 | 2020 |
| Stochastic proximal langevin algorithm: Potential splitting and nonasymptotic rates A Salim, D Kovalev, P Richtárik arXiv preprint arXiv:1905.11768, 2019 | 9 | 2019 |
| Fast linear convergence of randomized BFGS D Kovalev, RM Gower, P Richtárik, A Rogozin arXiv preprint arXiv:2002.11337, 2020 | 8 | 2020 |
| Towards accelerated rates for distributed optimization over time-varying networks A Rogozin, V Lukoshkin, A Gasnikov, D Kovalev, E Shulgin arXiv preprint arXiv:2009.11069, 2020 | 7 | 2020 |
| Stochastic spectral and conjugate descent methods D Kovalev, E Gorbunov, E Gasanov, P Richtárik arXiv preprint arXiv:1802.03703, 2018 | 7 | 2018 |
| Decentralized distributed optimization for saddle point problems A Rogozin, A Beznosikov, D Dvinskikh, D Kovalev, P Dvurechensky, ... arXiv preprint arXiv:2102.07758, 2021 | 5 | 2021 |
| Accelerated methods for saddle-point problem MS Alkousa, AV Gasnikov, DM Dvinskikh, DA Kovalev, FS Stonyakin Computational Mathematics and Mathematical Physics 60 (11), 1787-1809, 2020 | 5 | 2020 |
| Distributed fixed point methods with compressed iterates S Chraibi, A Khaled, D Kovalev, P Richtárik, A Salim, M Takáè arXiv preprint arXiv:1912.09925, 2019 | 4 | 2019 |
| Variance reduced coordinate descent with acceleration: New method with a surprising application to finite-sum problems F Hanzely, D Kovalev, P Richtarik International Conference on Machine Learning, 4039-4048, 2020 | 3 | 2020 |
| A hypothesis about the rate of global convergence for optimal methods (Newton's type) in smooth convex optimization AV Gasnikov, DA Kovalev Computer research and modeling 10 (3), 305-314, 2018 | 3 | 2018 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
