Antonio Orvieto

92 papers A* 26A 8B 1Journal 57

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Deriving Hyperparameter Scaling Laws via Modern Optimization Theory. CoRR Egor Shulgin, Dimitri von Rütte, Tianyue H. Zhang, Niccolò Ajroldi, Bernhard Schölkopf, Antonio Orvieto
2026	J	`jnl`	Explaining Grokking in Transformers through the Lens of Inductive Bias. CoRR Jaisidh Singh, Diganta Misra, Antonio Orvieto
2026	J	`jnl`	GASP: Guided Asymmetric Self-Play For Coding LLMs. CoRR Swadesh Jana, Cansu Sancaktar, Tomás Danis, Georg Martius, Antonio Orvieto, Pavel Kolev
2026	J	`jnl`	Improved state mixing in higher-order and block diagonal linear recurrent networks. CoRR Igor Dubinin, Antonio Orvieto, Felix Effenberger
2026	J	`jnl`	Universal Dynamics of Warmup Stable Decay: understanding WSD beyond Transformers. CoRR Annalisa Belloni, Lorenzo Noci, Antonio Orvieto
2025	A*	`conf`	(Almost) Free Modality Stitching of Foundation Models. EMNLP Jaisidh Singh, Diganta Misra, Boris Knyazev, Antonio Orvieto
2025	J	`jnl`	(Almost) Free Modality Stitching of Foundation Models. CoRR Jaisidh Singh, Diganta Misra, Boris Knyazev, Antonio Orvieto
2025	J	`jnl`	Adam Simplified: Bias Correction Debunked. CoRR Sam Laing, Antonio Orvieto
2025	A*	`conf`	Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise. ICLR Enea Monzio Compagnoni, Tianlin Liu, Rustem Islamov, Frank Norbert Proske, Antonio Orvieto, Aurélien Lucchi
2025	J	`jnl`	An Uncertainty Principle for Linear Recurrent Neural Networks. CoRR Alexandre François, Antonio Orvieto, Francis R. Bach
2025	J	`jnl`	An accelerated lyapunov function for Polyak's Heavy-ball on convex quadratics. Optim. Lett. Antonio Orvieto
2025	A*	`conf`	An uncertainty principle for Linear Recurrent Neural Networks. COLT Alexandre François, Antonio Orvieto, Francis R. Bach
2025	J	`jnl`	Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models. CoRR Fay Elhassan, Niccolò Ajroldi, Antonio Orvieto, Jonas Geiping
2025	J	`jnl`	Design Principles for Sequence Models via Coefficient Dynamics. CoRR Jerome Sieber, Antonio Orvieto, Melanie N. Zeilinger, Carmen Amo Alonso
2025	J	`jnl`	Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size. CoRR Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurélien Lucchi
2025	J	`jnl`	Fixed-Point RNNs: From Diagonal to Dense in a Few Iterations. CoRR Sajad Movahedi, Felix Sarnthein, Nicola Muca Cirone, Antonio Orvieto
2025	A*	`conf`	Generalized Interpolating Discrete Diffusion. ICML Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann
2025	J	`jnl`	Generalized Interpolating Discrete Diffusion. CoRR Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann
2025	J	`jnl`	Generalized Linear Mode Connectivity for Transformers. CoRR Alexander Theus, Alessandro Cabodi, Sotiris Anagnostidis, Antonio Orvieto, Sidak Pal Singh, Valentina Boeva
2025	A*	`conf`	Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture. ICLR Sajad Movahedi, Antonio Orvieto, Seyed-Mohsen Moosavi-Dezfooli
2025	J	`jnl`	GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities. CoRR 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
2025	J	`jnl`	Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere? CoRR Si Yi Meng, Baptiste Goujaud, Antonio Orvieto, Christopher De Sa
2025	J	`jnl`	How does the optimizer implicitly bias the model merging loss landscape? CoRR Chenxiang Zhang, Alexander Theus, Damien Teney, Antonio Orvieto, Jun Pang, Sjouke Mauw
2025	J	`jnl`	In Search of Adam's Secret Sauce. CoRR Antonio Orvieto, Robert Gower
2025	J	`jnl`	Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling. CoRR Teodora Sreckovic, Jonas Geiping, Antonio Orvieto
2025	J	`jnl`	On the Interaction of Noise, Compression Role, and Adaptivity under (L CoRR Enea Monzio Compagnoni, Rustem Islamov, Antonio Orvieto, Eduard Gorbunov
2025	J	`jnl`	Scaling Behavior of Discrete Diffusion Language Models. CoRR Dimitri von Rütte, Janis Fluri, Omead Pooladzandi, Bernhard Schölkopf, Thomas Hofmann, Antonio Orvieto
2025	J	`jnl`	Selective Rotary Position Embedding. CoRR Sajad Movahedi, Timur Carstensen, Arshia Afzal, Frank Hutter, Antonio Orvieto, Volkan Cevher
2025	J	`jnl`	When recalling in-context, Transformers are not SSMs. CoRR Destiny Okpekpe, Antonio Orvieto
2025	A*	`conf`	When, Where and Why to Average Weights? ICML Niccolò Ajroldi, Antonio Orvieto, Jonas Geiping
2025	J	`jnl`	When, Where and Why to Average Weights? CoRR Niccolò Ajroldi, Antonio Orvieto, Jonas Geiping
2024	J	`jnl`	Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise. CoRR Enea Monzio Compagnoni, Tianlin Liu, Rustem Islamov, Frank Norbert Proske, Antonio Orvieto, Aurélien Lucchi
2024	J	`jnl`	An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes. CoRR Antonio Orvieto, Lin Xiao
2024	J	`jnl`	Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture. CoRR Sajad Movahedi, Antonio Orvieto, Seyed-Mohsen Moosavi-Dezfooli
2024	J	`jnl`	Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes. CoRR Si Yi Meng, Antonio Orvieto, Daniel Yiming Cao, Christopher De Sa
2024	A*	`conf`	Loss Landscape Characterization of Neural Networks without Over-Parametrization. NeurIPS Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurélien Lucchi
2024	J	`jnl`	Loss Landscape Characterization of Neural Networks without Over-Parametrization. CoRR Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurélien Lucchi
2024	J	`jnl`	NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs. CoRR Nursena Köprücü, Destiny Okpekpe, Antonio Orvieto
2024	J	`jnl`	On the low-shot transferability of [V]-Mamba. CoRR Diganta Misra, Jay Gala, Antonio Orvieto
2024	A*	`conf`	Recurrent Distance Filtering for Graph Representation Learning. ICML Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann
2024	A*	`conf`	Recurrent neural networks: vanishing and exploding gradients are not the end of the story. NeurIPS Nicolas Zucchet, Antonio Orvieto
2024	J	`jnl`	Recurrent neural networks: vanishing and exploding gradients are not the end of the story. CoRR Nicolas Zucchet, Antonio Orvieto
2024	A	`conf`	SDEs for Minimax Optimization. AISTATS Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurélien Lucchi
2024	J	`jnl`	SDEs for Minimax Optimization. CoRR Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Norbert Proske, Aurélien Lucchi
2024	A*	`conf`	Super Consistency of Neural Network Landscapes and Learning Rate Transfer. NeurIPS Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto
2024	A*	`conf`	Theoretical Foundations of Deep Selective State-Space Models. NeurIPS Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, Terry J. Lyons
2024	J	`jnl`	Theoretical Foundations of Deep Selective State-Space Models. CoRR Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, Terry J. Lyons
2024	A*	`conf`	Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks. NeurIPS Jerome Sieber, Carmen Amo Alonso, Alexandre Didier, Melanie N. Zeilinger, Antonio Orvieto
2024	J	`jnl`	Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks. CoRR Jerome Sieber, Carmen Amo Alonso, Alexandre Didier, Melanie N. Zeilinger, Antonio Orvieto
2024	A*	`conf`	Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues. ICML Antonio Orvieto, Soham De, Caglar Gulcehre, Razvan Pascanu, Samuel L. Smith
2024	J	`jnl`	Why do Learning Rates Transfer? Reconciling Optimization and Scaling Limits for Deep Learning. CoRR Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto
2023	A*	`conf`	Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. CVPR Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann
2023	J	`jnl`	Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. CoRR Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann
2023	A*	`conf`	An SDE for Modeling SAM: Theory and Insights. ICML Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi
2023	J	`jnl`	An SDE for Modeling SAM: Theory and Insights. CoRR Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi
2023	A	`conf`	Explicit Regularization in Overparametrized Models via Noise Injection. AISTATS Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach
2023	J	`jnl`	On the Universality of Linear Recurrences Followed by Nonlinear Projections. CoRR Antonio Orvieto, Soham De, Çaglar Gülçehre, Razvan Pascanu, Samuel L. Smith
2023	B	`conf`	On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. IJCNN Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann
2023	J	`jnl`	Recurrent Distance-Encoding Neural Networks for Graph Representation Learning. CoRR Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann
2023	A*	`conf`	Resurrecting Recurrent Neural Networks for Long Sequences. ICML Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De
2023	J	`jnl`	Resurrecting Recurrent Neural Networks for Long Sequences. CoRR Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De
2022	A*	`conf`	Anticorrelated Noise Injection for Improved Generalization. ICML Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi
2022	J	`jnl`	Anticorrelated Noise Injection for Improved Generalization. CoRR Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi
2022	A*	`conf`	Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. NeurIPS Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou
2022	J	`jnl`	Explicit Regularization in Overparametrized Models via Noise Injection. CoRR Antonio Orvieto, Anant Raj, Hans Kersting, Francis R. Bach
2022	A	`conf`	Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
2022	A*	`conf`	On the Theoretical Properties of Noise Correlation in Stochastic Optimization. NeurIPS Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting
2022	J	`jnl`	On the Theoretical Properties of Noise Correlation in Stochastic Optimization. CoRR Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting
2022	J	`jnl`	Randomized Signature Layers for Signal Extraction in Time Series Data. CoRR Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann
2022	A*	`conf`	Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. NeurIPS Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi
2022	J	`jnl`	Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. CoRR Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi
2022	A	`conf`	Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi
2021	J	`jnl`	Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
2021	A*	`conf`	Learning explanations that are hard to vary. ICLR Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf
2021	A	`conf`	Momentum Improves Optimization on Riemannian Manifolds. AISTATS Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi
2021	A*	`conf`	On the Second-order Convergence Properties of Random Search Methods. NeurIPS Aurélien Lucchi, Antonio Orvieto, Adamos Solomou
2021	J	`jnl`	On the Second-order Convergence Properties of Random Search Methods. CoRR Aurélien Lucchi, Antonio Orvieto, Adamos Solomou
2021	A*	`conf`	Rethinking the Variational Interpretation of Accelerated Optimization Methods. NeurIPS Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand
2021	A	`conf`	Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. AISTATS Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith
2021	J	`jnl`	Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization. CoRR Peiyuan Zhang, Antonio Orvieto, Hadi Daneshmand, Thomas Hofmann, Roy S. Smith
2021	J	`jnl`	Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi
2020	A	`conf`	A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. AISTATS Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi
2020	A*	`conf`	An Accelerated DFO Algorithm for Finite-sum Convex Functions. ICML Yuwen Chen, Antonio Orvieto, Aurélien Lucchi
2020	J	`jnl`	An Accelerated DFO Algorithm for Finite-sum Convex Functions. CoRR Yuwen Chen, Antonio Orvieto, Aurélien Lucchi
2020	J	`jnl`	Learning explanations that are hard to vary. CoRR Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf
2020	J	`jnl`	Two-Level K-FAC Preconditioning for Deep Learning. CoRR Nikolaos Tselepidis, Jonas Kohler, Antonio Orvieto
2019	A*	`conf`	Continuous-time Models for Stochastic Optimization Algorithms. NeurIPS Antonio Orvieto, Aurélien Lucchi
2019	A*	`conf`	Shadowing Properties of Optimization Algorithms. NeurIPS Antonio Orvieto, Aurélien Lucchi
2019	J	`jnl`	Shadowing Properties of Optimization Algorithms. CoRR Antonio Orvieto, Aurélien Lucchi
2019	A	`conf`	The Role of Memory in Stochastic Optimization. UAI Antonio Orvieto, Jonas Kohler, Aurélien Lucchi
2019	J	`jnl`	The Role of Memory in Stochastic Optimization. CoRR Antonio Orvieto, Jonas Kohler, Aurélien Lucchi
2018	J	`jnl`	Continuous-time Models for Stochastic Optimization Algorithms. CoRR Antonio Orvieto, Aurélien Lucchi