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ORCIDAntonio Orvieto
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2020 – today
- 2024
[c24]Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurélien Lucchi:
SDEs for Minimax Optimization. AISTATS 2024: 4834-4842- [c23]Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann:
Recurrent Distance Filtering for Graph Representation Learning. ICML 2024 - [c22]Antonio Orvieto, Soham De, Caglar Gulcehre, Razvan Pascanu, Samuel L. Smith:
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues. ICML 2024 - [i28]Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Norbert Proske, Aurélien Lucchi:
SDEs for Minimax Optimization. CoRR abs/2402.12508 (2024) - [i27]Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto:
Why do Learning Rates Transfer? Reconciling Optimization and Scaling Limits for Deep Learning. CoRR abs/2402.17457 (2024) - [i26]Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, Terry J. Lyons:
Theoretical Foundations of Deep Selective State-Space Models. CoRR abs/2402.19047 (2024) - [i25]Diganta Misra, Jay Gala, Antonio Orvieto:
On the low-shot transferability of [V]-Mamba. CoRR abs/2403.10696 (2024) - [i24]Jerome Sieber, Carmen Amo Alonso, Alexandre Didier
, Melanie N. Zeilinger, Antonio Orvieto:
Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks. CoRR abs/2405.15731 (2024) - [i23]Nicolas Zucchet, Antonio Orvieto:
Recurrent neural networks: vanishing and exploding gradients are not the end of the story. CoRR abs/2405.21064 (2024) - [i22]Si Yi Meng, Antonio Orvieto, Daniel Yiming Cao, Christopher De Sa:
Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes. CoRR abs/2406.05033 (2024) - [i21]Antonio Orvieto, Lin Xiao:
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes. CoRR abs/2407.04358 (2024) - 2023
- [c21]Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach:
Explicit Regularization in Overparametrized Models via Noise Injection. AISTATS 2023: 7265-7287 - [c20]Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann:
Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. CVPR 2023: 11930-11939 - [c19]Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi:
An SDE for Modeling SAM: Theory and Insights. ICML 2023: 25209-25253 - [c18]Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De:
Resurrecting Recurrent Neural Networks for Long Sequences. ICML 2023: 26670-26698 - [c17]Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann:
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. IJCNN 2023: 1-8 - [i20]Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi:
An SDE for Modeling SAM: Theory and Insights. CoRR abs/2301.08203 (2023) - [i19]Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De:
Resurrecting Recurrent Neural Networks for Long Sequences. CoRR abs/2303.06349 (2023) - [i18]Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann:
Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. CoRR abs/2303.09483 (2023) - [i17]Antonio Orvieto, Soham De, Çaglar Gülçehre, Razvan Pascanu, Samuel L. Smith:
On the Universality of Linear Recurrences Followed by Nonlinear Projections. CoRR abs/2307.11888 (2023) - [i16]Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann:
Recurrent Distance-Encoding Neural Networks for Graph Representation Learning. CoRR abs/2312.01538 (2023) - 2022
- [c16]Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS 2022: 5485-5517 - [c15]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS 2022: 7942-7975 - [c14]Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi:
Anticorrelated Noise Injection for Improved Generalization. ICML 2022: 17094-17116 - [c13]Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. NeurIPS 2022 - [c12]Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi:
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. NeurIPS 2022 - [c11]Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou:
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. NeurIPS 2022 - [i15]Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann:
Randomized Signature Layers for Signal Extraction in Time Series Data. CoRR abs/2201.00384 (2022) - [i14]Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi:
Anticorrelated Noise Injection for Improved Generalization. CoRR abs/2202.02831 (2022) - [i13]Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi:
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. CoRR abs/2206.03126 (2022) - [i12]Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach:
Explicit Regularization in Overparametrized Models via Noise Injection. CoRR abs/2206.04613 (2022) - [i11]Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. CoRR abs/2209.09162 (2022) - 2021
- [c10]Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
Momentum Improves Optimization on Riemannian Manifolds. AISTATS 2021: 1351-1359 - [c9]Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith:
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. AISTATS 2021: 3979-3987 - [c8]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. ICLR 2021 - [c7]Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand:
Rethinking the Variational Interpretation of Accelerated Optimization Methods. NeurIPS 2021: 14396-14406 - [c6]Aurélien Lucchi, Antonio Orvieto, Adamos Solomou:
On the Second-order Convergence Properties of Random Search Methods. NeurIPS 2021: 25633-25645 - [i10]Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith:
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. CoRR abs/2102.11537 (2021) - [i9]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR abs/2106.03763 (2021) - [i8]Aurélien Lucchi, Antonio Orvieto, Adamos Solomou:
On the Second-order Convergence Properties of Random Search Methods. CoRR abs/2110.13265 (2021) - [i7]Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR abs/2112.05604 (2021) - 2020
- [c5]Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. AISTATS 2020: 1297-1307 - [c4]Yuwen Chen, Antonio Orvieto, Aurélien Lucchi:
An Accelerated DFO Algorithm for Finite-sum Convex Functions. ICML 2020: 1681-1690 - [i6]Yuwen Chen, Antonio Orvieto, Aurélien Lucchi:
An Accelerated DFO Algorithm for Finite-sum Convex Functions. CoRR abs/2007.03311 (2020) - [i5]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. CoRR abs/2009.00329 (2020) - [i4]Nikolaos Tselepidis, Jonas Kohler, Antonio Orvieto:
Two-Level K-FAC Preconditioning for Deep Learning. CoRR abs/2011.00573 (2020)
2010 – 2019
- 2019
- [c3]Antonio Orvieto, Aurélien Lucchi:
Continuous-time Models for Stochastic Optimization Algorithms. NeurIPS 2019: 12589-12601 - [c2]Antonio Orvieto, Aurélien Lucchi:
Shadowing Properties of Optimization Algorithms. NeurIPS 2019: 12671-12682 - [c1]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. UAI 2019: 356-366 - [i3]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. CoRR abs/1907.01678 (2019) - [i2]Antonio Orvieto, Aurélien Lucchi:
Shadowing Properties of Optimization Algorithms. CoRR abs/1911.05206 (2019) - 2018
- [i1]Antonio Orvieto, Aurélien Lucchi:
Continuous-time Models for Stochastic Optimization Algorithms. CoRR abs/1810.02565 (2018)
Coauthor Index
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last updated on 2024-10-07 22:20 CEST by the dblp team
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