Alessandro Favero

According to our database, Alessandro Favero authored at least 19 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Learn from your own latents and not from tokens: A sample-complexity theory.

Daniel J. Korchinski, Alessandro Favero, Matthieu Wyart

CoRR, May, 2026

2025

Task Addition and Weight Disentanglement in Closed-Vocabulary Models.

Adam Hazimeh, Alessandro Favero, Pascal Frossard

CoRR, November, 2025

Backdoor Unlearning by Linear Task Decomposition.

Amel Abdelraheem, Alessandro Favero, Gérôme Bovet, Pascal Frossard

CoRR, October, 2025

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning.

Alessandro Favero

CoRR, October, 2025

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models.

Alessandro Favero, Antonio Sclocchi, Matthieu Wyart

CoRR, May, 2025

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures.

Francesco Cagnetta, Alessandro Favero, Antonio Sclocchi, Matthieu Wyart

CoRR, May, 2025

MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs.

Ke Wang, Yiming Qin, Nikolaos Dimitriadis, Alessandro Favero, Pascal Frossard

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

How Compositional Generalization and Creativity Improve as Diffusion Models are Trained.

Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta, Pascal Frossard, Matthieu Wyart

Proceedings of the Forty-second International Conference on Machine Learning, 2025

LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging.

Ke Wang, Nikolaos Dimitriadis, Alessandro Favero, Guillermo Ortiz-Jiménez, François Fleuret, Pascal Frossard

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Probing the Latent Hierarchical Structure of Data via Diffusion Models.

Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi, Matthieu Wyart

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data.

Antonio Sclocchi, Alessandro Favero, Matthieu Wyart

CoRR, 2024

Multi-Modal Hallucination Control by Visual Information Grounding.

Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary, Pramuditha Perera, Alessandro Achille, Ashwin Swaminathan, Stefano Soatto

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model.

Leonardo Petrini, Francesco Cagnetta, Umberto M. Tomasini, Alessandro Favero, Matthieu Wyart

CoRR, 2023

Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models.

Guillermo Ortiz-Jiménez, Alessandro Favero, Pascal Frossard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

What Can Be Learnt With Wide Convolutional Neural Networks?

Francesco Cagnetta, Alessandro Favero, Matthieu Wyart

Proceedings of the International Conference on Machine Learning, 2023

2022

How Wide Convolutional Neural Networks Learn Hierarchical Tasks.

Francesco Cagnetta, Alessandro Favero, Matthieu Wyart

CoRR, 2022

2021

Relative stability toward diffeomorphisms in deep nets indicates performance.

Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart

CoRR, 2021

Relative stability toward diffeomorphisms indicates performance in deep nets.

Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios.

Alessandro Favero, Francesco Cagnetta, Matthieu Wyart

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021