| Learning explanations that are hard to vary G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf International Conference on Learning Representations (2021), 2020 | 8 | 2020 |
| Shadowing properties of optimization algorithms A Orvieto, A Lucchi Advances in Neural Information Processing Systems 32 (2019), 2019 | 8 | 2019 |
| Continuous-time models for stochastic optimization algorithms A Orvieto, A Lucchi Advances in Neural Information Processing Systems 32 (2019), 2018 | 8 | 2018 |
| A continuous-time perspective for modeling acceleration in riemannian optimization F Alimisis, A Orvieto, G Bécigneul, A Lucchi International Conference on Artificial Intelligence and Statistics, 1297-1307, 2020 | 7 | 2020 |
| The role of memory in stochastic optimization A Orvieto, J Kohler, A Lucchi Uncertainty in Artificial Intelligence, 356-366, 2020 | 6 | 2020 |
| Practical accelerated optimization on Riemannian manifolds F Alimisis, A Orvieto, G Bécigneul, A Lucchi International Conference on Artificial Intelligence and Statistics (2021), 2020 | 3 | 2020 |
| An accelerated DFO algorithm for finite-sum convex functions C Yuwen, A Orvieto, A Lucchi 37th International Conference on Machine Learning (ICML 2020)(virtual), 2020 | 2* | 2020 |
| Momentum Improves Optimization on Riemannian Manifolds F Alimisis, A Orvieto, G Becigneul, A Lucchi International Conference on Artificial Intelligence and Statistics, 1351-1359, 2021 | | 2021 |
| Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization P Zhang, A Orvieto, H Daneshmand, T Hofmann, R Smith International Conference on Artificial Intelligence and Statistics (2021), 2021 | | 2021 |
| Two-Level K-FAC Preconditioning for Deep Learning N Tselepidis, J Kohler, A Orvieto NeurIPS 2020 Workshop on Optimization for Machine Learning (OPT2020), 2020 | | 2020 |
Aurelien LucchiETH ZurichVerified email at inf.ethz.ch
