Antonio Orvieto
Title
Cited by
Cited by
Year
Learning explanations that are hard to vary
G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf
International Conference on Learning Representations (2021), 2020
202020
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
122020
The role of memory in stochastic optimization
A Orvieto, J Kohler, A Lucchi
Uncertainty in Artificial Intelligence, 356-366, 2020
102020
Shadowing properties of optimization algorithms
A Orvieto, A Lucchi
Advances in Neural Information Processing Systems 32 (2019), 2019
102019
Continuous-time models for stochastic optimization algorithms
A Orvieto, A Lucchi
Advances in Neural Information Processing Systems 32 (2019), 2018
102018
Momentum Improves Optimization on Riemannian Manifolds
F Alimisis, A Orvieto, G Bécigneul, A Lucchi
International Conference on Artificial Intelligence and Statistics, 1351-1359, 2021
9*2021
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
5*2020
Rethinking the Variational Interpretation of Nesterov's Accelerated Method
P Zhang, A Orvieto, H Daneshmand
arXiv preprint arXiv:2107.05040, 2021
2021
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks
A Orvieto, J Kohler, D Pavllo, T Hofmann, A Lucchi
arXiv preprint arXiv:2106.03763, 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
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Articles 1–11