Continuous-time Models for Stochastic Optimization Algorithms. - Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2019)

Shadowing Properties of Optimization Algorithms. - Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2019)

The Role of Memory in Stochastic Optimization. - Antonio Orvieto; Jonas Kohler; Aurélien Lucchi (UAI, 2019)

A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. - Foivos Alimisis; Antonio Orvieto; Gary Bécigneul; Aurélien Lucchi (AISTATS, 2020)

An Accelerated DFO Algorithm for Finite-sum Convex Functions. - Yuwen Chen; Antonio Orvieto; Aurélien Lucchi (ICML, 2020)

Two-Level K-FAC Preconditioning for Deep Learning. - Nikolaos Tselepidis; Jonas Kohler; Antonio Orvieto (CoRR, 2020)

Momentum Improves Optimization on Riemannian Manifolds. - Foivos Alimisis; Antonio Orvieto; Gary Bécigneul; Aurélien Lucchi (AISTATS, 2021)

Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. - Peiyuan Zhang; Antonio Orvieto; Hadi Daneshmand; Thomas Hofmann 001; Roy S. Smith (AISTATS, 2021)

Learning explanations that are hard to vary. - Giambattista Parascandolo; Alexander Neitz; Antonio Orvieto; Luigi Gresele; Bernhard Schölkopf (ICLR, 2021)

On the Second-order Convergence Properties of Random Search Methods. - Aurélien Lucchi; Antonio Orvieto; Adamos Solomou (NeurIPS, 2021)

Rethinking the Variational Interpretation of Accelerated Optimization Methods. - Peiyuan Zhang; Antonio Orvieto; Hadi Daneshmand (NeurIPS, 2021)

Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. - Antonio Orvieto; Jonas Kohler; Dario Pavllo; Thomas Hofmann 001; Aurélien Lucchi (CoRR, 2021)

Vanishing Curvature in Randomly Initialized Deep ReLU Networks. - Antonio Orvieto; Jonas Kohler; Dario Pavllo; Thomas Hofmann 001; Aurélien Lucchi (AISTATS, 2022)

Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. - Junchi Yang; Antonio Orvieto; Aurélien Lucchi; Niao He (AISTATS, 2022)

Anticorrelated Noise Injection for Improved Generalization. - Antonio Orvieto; Hans Kersting; Frank Proske; Francis R. Bach; Aurélien Lucchi (ICML, 2022)

On the Theoretical Properties of Noise Correlation in Stochastic Optimization. - Aurélien Lucchi; Frank Proske; Antonio Orvieto; Francis R. Bach; Hans Kersting (NeurIPS, 2022)

Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. - Lorenzo Noci; Sotiris Anagnostidis; Luca Biggio; Antonio Orvieto; Sidak Pal Singh; Aurélien Lucchi (NeurIPS, 2022)

Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. - Antonio Orvieto; Simon Lacoste-Julien; Nicolas Loizou (NeurIPS, 2022)

Randomized Signature Layers for Signal Extraction in Time Series Data. - Enea Monzio Compagnoni; Luca Biggio; Antonio Orvieto; Thomas Hofmann 001; Josef Teichmann (CoRR, 2022)

Explicit Regularization in Overparametrized Models via Noise Injection. - Antonio Orvieto; Anant Raj; Hans Kersting; Francis R. Bach (AISTATS, 2023)

Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. - Sanghwan Kim; Lorenzo Noci; Antonio Orvieto; Thomas Hofmann 001 (CVPR, 2023)

An SDE for Modeling SAM: Theory and Insights. - Enea Monzio Compagnoni; Luca Biggio; Antonio Orvieto; Frank Norbert Proske; Hans Kersting; Aurélien Lucchi (ICML, 2023)

Resurrecting Recurrent Neural Networks for Long Sequences. - Antonio Orvieto; Samuel L. Smith; Albert Gu; Anushan Fernando; Çaglar Gülçehre; Razvan Pascanu; Soham De (ICML, 2023)

On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. - Enea Monzio Compagnoni; Anna Scampicchio; Luca Biggio; Antonio Orvieto; Thomas Hofmann 001; Josef Teichmann (IJCNN, 2023)

On the Universality of Linear Recurrences Followed by Nonlinear Projections. - Antonio Orvieto; Soham De; Çaglar Gülçehre; Razvan Pascanu; Samuel L. Smith (CoRR, 2023)

Recurrent Distance-Encoding Neural Networks for Graph Representation Learning. - Yuhui Ding; Antonio Orvieto; Bobby He; Thomas Hofmann 001 (CoRR, 2023)

SDEs for Minimax Optimization. - Enea Monzio Compagnoni; Antonio Orvieto; Hans Kersting; Frank Proske; Aurélien Lucchi (AISTATS, 2024)

Recurrent Distance Filtering for Graph Representation Learning. - Yuhui Ding; Antonio Orvieto; Bobby He; Thomas Hofmann 001 (ICML, 2024)

Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues. - Antonio Orvieto; Soham De; Caglar Gulcehre; Razvan Pascanu; Samuel L. Smith (ICML, 2024)

Theoretical Foundations of Deep Selective State-Space Models. - Nicola Muca Cirone; Antonio Orvieto; Benjamin Walker 001; Cristopher Salvi; Terry J. Lyons (NeurIPS, 2024)

Loss Landscape Characterization of Neural Networks without Over-Parametrization. - Rustem Islamov; Niccolò Ajroldi; Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2024)

Super Consistency of Neural Network Landscapes and Learning Rate Transfer. - Lorenzo Noci; Alexandru Meterez; Thomas Hofmann 001; Antonio Orvieto (NeurIPS, 2024)

Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks. - Jerome Sieber; Carmen Amo Alonso; Alexandre Didier; Melanie N. Zeilinger; Antonio Orvieto (NeurIPS, 2024)

Recurrent neural networks: vanishing and exploding gradients are not the end of the story. - Nicolas Zucchet; Antonio Orvieto (NeurIPS, 2024)

Why do Learning Rates Transfer? Reconciling Optimization and Scaling Limits for Deep Learning. - Lorenzo Noci; Alexandru Meterez; Thomas Hofmann 001; Antonio Orvieto (CoRR, 2024)

On the low-shot transferability of [V]-Mamba. - Diganta Misra; Jay Gala; Antonio Orvieto (CoRR, 2024)

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes. - Si Yi Meng; Antonio Orvieto; Daniel Yiming Cao; Christopher De Sa (CoRR, 2024)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes. - Antonio Orvieto; Lin Xiao (CoRR, 2024)

NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs. - Nursena Köprücü; Destiny Okpekpe; Antonio Orvieto (CoRR, 2024)

An uncertainty principle for Linear Recurrent Neural Networks. - Alexandre François; Antonio Orvieto; Francis R. Bach (COLT, 2025)

Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise. - Enea Monzio Compagnoni; Tianlin Liu; Rustem Islamov; Frank Norbert Proske; Antonio Orvieto; Aurélien Lucchi (ICLR, 2025)

Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture. - Sajad Movahedi; Antonio Orvieto; Seyed-Mohsen Moosavi-Dezfooli (ICLR, 2025)

When, Where and Why to Average Weights? - Niccolò Ajroldi; Antonio Orvieto; Jonas Geiping (CoRR, 2025)

Generalized Interpolating Discrete Diffusion. - Dimitri von Rütte; Janis Fluri; Yuhui Ding; Antonio Orvieto; Bernhard Schölkopf; Thomas Hofmann 001 (CoRR, 2025)

Fixed-Point RNNs: From Diagonal to Dense in a Few Iterations. - Sajad Movahedi; Felix Sarnthein; Nicola Muca Cirone; Antonio Orvieto (CoRR, 2025)

An accelerated lyapunov function for Polyak's Heavy-ball on convex quadratics. - Antonio Orvieto (Optim. Lett., 2025)

Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models. - Fay Elhassan; Niccolò Ajroldi; Antonio Orvieto; Jonas Geiping (CoRR, 2025)

In Search of Adam's Secret Sauce. - Antonio Orvieto; Robert Gower (CoRR, 2025)

On the Interaction of Noise, Compression Role, and Adaptivity under (L0, L1)-Smoothness: An SDE-based Approach. - Enea Monzio Compagnoni; Rustem Islamov; Antonio Orvieto; Eduard Gorbunov (CoRR, 2025)

Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling. - Teodora Sreckovic; Jonas Geiping; Antonio Orvieto (CoRR, 2025)

Generalized Linear Mode Connectivity for Transformers. - Alexander Theus; Alessandro Cabodi; Sotiris Anagnostidis; Antonio Orvieto; Sidak Pal Singh; Valentina Boeva (CoRR, 2025)

(Almost) Free Modality Stitching of Foundation Models. - Jaisidh Singh; Diganta Misra; Boris Knyazev; Antonio Orvieto (CoRR, 2025)

Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere? - Si Yi Meng; Baptiste Goujaud; Antonio Orvieto; Christopher De Sa (CoRR, 2025)

GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities. - Diganta Misra; Nizar Islah; Victor May; Brice Rauby; Zihan Wang; Justine Gehring; Antonio Orvieto; Muawiz Chaudhary; Eilif B. Muller; Irina Rish; Samira Ebrahimi Kahou; Massimo Caccia (CoRR, 2025)