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Antonio Orvieto
Antonio Orvieto
PhD student, ETH Zürich
Dirección de correo verificada de ethz.ch - Página principal
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Learning explanations that are hard to vary
G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf
International Conference on Learning Representations (2021), 2020
412020
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
182020
Momentum improves optimization on Riemannian manifolds
F Alimisis, A Orvieto, G Becigneul, A Lucchi
International Conference on Artificial Intelligence and Statistics, 1351-1359, 2021
17*2021
The role of memory in stochastic optimization
A Orvieto, J Kohler, A Lucchi
Uncertainty in Artificial Intelligence, 356-366, 2020
132020
Continuous-time models for stochastic optimization algorithms
A Orvieto, A Lucchi
Advances in Neural Information Processing Systems 32 (2019), 2018
132018
Shadowing properties of optimization algorithms
A Orvieto, A Lucchi
Advances in Neural Information Processing Systems 32 (2019), 2019
112019
An accelerated dfo algorithm for finite-sum convex functions
Y Chen, A Orvieto, A Lucchi
International Conference on Machine Learning (ICML), 2020, 2020
102020
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
12022
On the Second-order Convergence Properties of Random Search Methods
A Lucchi, A Orvieto, A Solomou
Advances in Neural Information Processing Systems 34, 2021
12021
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
12021
Two-Level K-FAC Preconditioning for Deep Learning
N Tselepidis, J Kohler, A Orvieto
NeurIPS 2020 Workshop on Optimization for Machine Learning (OPT2020), 2020
12020
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution
A Orvieto, S Lacoste-Julien, N Loizou
arXiv preprint arXiv:2205.04583, 2022
2022
Vanishing Curvature in Randomly Initialized Deep ReLU Networks
A Orvieto, J Kohler, D Pavllo, T Hofmann, A Lucchi
International Conference on Artificial Intelligence and Statistics, 7942-7975, 2022
2022
Anticorrelated Noise Injection for Improved Generalization
A Orvieto, H Kersting, F Proske, F Bach, A Lucchi
arXiv preprint arXiv:2202.02831, 2022
2022
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
2022
Rethinking the Variational Interpretation of Accelerated Optimization Methods
P Zhang, A Orvieto, H Daneshmand
Advances in Neural Information Processing Systems 34, 2021
2021
Empirics on the expressiveness of Randomized Signature
EM Compagnoni, L Biggio, A Orvieto
The Symbiosis of Deep Learning and Differential Equations, 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
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–18