| Stochastic model-based minimization of weakly convex functions D Davis, D Drusvyatskiy SIAM Journal on Optimization 29 (1), 207–239, 2018 | 455* | 2018 |
| Error bounds, quadratic growth, and linear convergence of proximal methods D Drusvyatskiy, AS Lewis Mathematics of Operations Research 43 (3), 919-948, 2018 | 352 | 2018 |
| Stochastic subgradient method converges on tame functions D Davis, D Drusvyatskiy, S Kakade, JD Lee Foundations of computational mathematics 20 (1), 119-154, 2020 | 272 | 2020 |
| Efficiency of minimizing compositions of convex functions and smooth maps D Drusvyatskiy, C Paquette Mathematical Programming 178, 503-558, 2019 | 240 | 2019 |
| Transversality and alternating projections for nonconvex sets D Drusvyatskiy, AD Ioffe, AS Lewis Foundations of Computational Mathematics 15 (6), 1637-1651, 2015 | 137* | 2015 |
| Subgradient methods for sharp weakly convex functions D Davis, D Drusvyatskiy, KJ MacPhee, C Paquette Journal of Optimization Theory and Applications 179, 962-982, 2018 | 117 | 2018 |
| The nonsmooth landscape of phase retrieval D Davis, D Drusvyatskiy, C Paquette IMA Journal of Numerical Analysis 40 (4), 2652-2695, 2020 | 110 | 2020 |
| Tilt stability, uniform quadratic growth, and strong metric regularity of the subdifferential D Drusvyatskiy, AS Lewis SIAM Journal on Optimization 23 (1), 256-267, 2013 | 105 | 2013 |
| Catalyst for gradient-based nonconvex optimization C Paquette, H Lin, D Drusvyatskiy, J Mairal, Z Harchaoui International Conference on Artificial Intelligence and Statistics, 613-622, 2018 | 101* | 2018 |
| Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence V Charisopoulos, Y Chen, D Davis, M Díaz, L Ding, D Drusvyatskiy Foundations of Computational Mathematics 21 (6), 1505-1593, 2021 | 99 | 2021 |
| The many faces of degeneracy in conic optimization D Drusvyatskiy, H Wolkowicz Foundations and Trends® in Optimization 3 (2), 77-170, 2017 | 94 | 2017 |
| Second-order growth, tilt stability, and metric regularity of the subdifferential D Drusvyatskiy, BS Mordukhovich, TTA Nghia arXiv preprint arXiv:1304.7385, 2013 | 91 | 2013 |
| Stochastic optimization with decision-dependent distributions D Drusvyatskiy, L Xiao Mathematics of Operations Research 48 (2), 954-998, 2023 | 77 | 2023 |
| An optimal first order method based on optimal quadratic averaging D Drusvyatskiy, M Fazel, S Roy SIAM Journal on Optimization 28 (1), 251-271, 2018 | 77 | 2018 |
| Level-set methods for convex optimization AY Aravkin, JV Burke, D Drusvyatskiy, MP Friedlander, S Roy Mathematical Programming 174, 359-390, 2019 | 72 | 2019 |
| Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria D Drusvyatskiy, AD Ioffe, AS Lewis Mathematical Programming 185, 357-383, 2021 | 68 | 2021 |
| The proximal point method revisited D Drusvyatskiy arXiv preprint arXiv:1712.06038, 2017 | 67 | 2017 |
| Multiplayer performative prediction: Learning in decision-dependent games A Narang, E Faulkner, D Drusvyatskiy, M Fazel, LJ Ratliff Journal of Machine Learning Research 24 (202), 1-56, 2023 | 58* | 2023 |
| Curves of descent D Drusvyatskiy, AD Ioffe, AS Lewis SIAM Journal on Control and Optimization 53 (1), 114-138, 2015 | 58 | 2015 |
| From low probability to high confidence in stochastic convex optimization D Davis, D Drusvyatskiy, L Xiao, J Zhang Journal of machine learning research 22 (49), 1-38, 2021 | 52* | 2021 |
