| Learning explanations that are hard to vary G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf International Conference on Learning Representations (2021), 2020 | 96 | 2020 |
| 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 | 42 | 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 | 36* | 2021 |
| Faster single-loop algorithms for minimax optimization without strong concavity J Yang, A Orvieto, A Lucchi, N He International Conference on Artificial Intelligence and Statistics, 5485-5517, 2022 | 26 | 2022 |
| Continuous-time models for stochastic optimization algorithms A Orvieto, A Lucchi Advances in Neural Information Processing Systems 32 (2019), 2018 | 25 | 2018 |
| Anticorrelated noise injection for improved generalization A Orvieto, H Kersting, F Proske, F Bach, A Lucchi International Conference on Machine Learning (ICML), 2022, 2022 | 15 | 2022 |
| The role of memory in stochastic optimization A Orvieto, J Kohler, A Lucchi Uncertainty in Artificial Intelligence, 356-366, 2020 | 15 | 2020 |
| An accelerated dfo algorithm for finite-sum convex functions Y Chen, A Orvieto, A Lucchi International Conference on Machine Learning (ICML), 2020, 2020 | 15 | 2020 |
| Shadowing properties of optimization algorithms A Orvieto, A Lucchi Advances in Neural Information Processing Systems 32 (2019), 2019 | 14 | 2019 |
| Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse L Noci, S Anagnostidis, L Biggio, A Orvieto, SP Singh, A Lucchi Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022 | 6 | 2022 |
| Explicit regularization in overparametrized models via noise injection A Orvieto, A Raj, H Kersting, F Bach International Conference on Artificial Intelligence and Statistics, 7265-7287, 2023 | 5 | 2023 |
| 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 | 5 | 2021 |
| Resurrecting recurrent neural networks for long sequences A Orvieto, SL Smith, A Gu, A Fernando, C Gulcehre, R Pascanu, S De arXiv preprint arXiv:2303.06349, 2023 | 4 | 2023 |
| Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution A Orvieto, S Lacoste-Julien, N Loizou Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022 | 4 | 2022 |
| On the Theoretical Properties of Noise Correlation in Stochastic Optimization A Lucchi, F Proske, A Orvieto, F Bach, H Kersting Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022 | 3 | 2022 |
| Vanishing Curvature in Randomly Initialized Deep ReLU Networks. A Orvieto, J Kohler, D Pavllo, T Hofmann, A Lucchi AISTATS, 7942-7975, 2022 | 3* | 2022 |
| On the second-order convergence properties of random search methods A Lucchi, A Orvieto, A Solomou Advances in Neural Information Processing Systems 34, 25633-25645, 2021 | 3 | 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 | 3 | 2020 |
| Randomized Signature Layers for Signal Extraction in Time Series Data E Monzio Compagnoni, L Biggio, A Orvieto, T Hofmann, J Teichmann arXiv e-prints, arXiv: 2201.00384, 2022 | 2* | 2022 |
| Rethinking the Variational Interpretation of Accelerated Optimization Methods P Zhang, A Orvieto, H Daneshmand Advances in Neural Information Processing Systems 34, 14396-14406, 2021 | 2* | 2021 |
Aurelien LucchiAssistant Professor, Department of Mathematics and Computer Science, University of BaselVerified email at inf.ethz.ch
