Unreasonable Effectiveness of Learning Neural Nets: Accessible States and Robust Ensembles. - Carlo Baldassi; Christian Borgs; Jennifer T. Chayes; Alessandro Ingrosso; Carlo Lucibello; Luca Saglietti; Riccardo Zecchina (CoRR, 2016)

Network reconstruction from infection cascades. - Alfredo Braunstein; Alessandro Ingrosso (CoRR, 2016)

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes. - Carlo Baldassi; Christian Borgs; Jennifer T. Chayes; Alessandro Ingrosso; Carlo Lucibello; Luca Saglietti; Riccardo Zecchina (Proc. Natl. Acad. Sci. USA, 2016)

Training dynamically balanced excitatory-inhibitory networks. - Alessandro Ingrosso; L. F. Abbott (CoRR, 2018)

Epidemic mitigation by statistical inference from contact tracing data. - Antoine Baker; Indaco Biazzo; Alfredo Braunstein; Giovanni Catania; Luca Dall'Asta; Alessandro Ingrosso; Florent Krzakala; Fabio Mazza; Marc Mézard; Anna Paola Muntoni; Maria Refinetti; Stefano Sarao Mannelli; et al. (CoRR, 2020)

Optimal learning with excitatory and inhibitory synapses. - Alessandro Ingrosso (PLoS Comput. Biol., 2020)

Input correlations impede suppression of chaos and learning in balanced rate networks. - Rainer Engelken; Alessandro Ingrosso; Ramin Khajeh; Sven Goedeke; L. F. Abbott (CoRR, 2022)

Input correlations impede suppression of chaos and learning in balanced firing-rate networks. - Rainer Engelken; Alessandro Ingrosso; Ramin Khajeh; Sven Goedeke; L. F. Abbott (PLoS Comput. Biol., 2022)

Neural networks trained with SGD learn distributions of increasing complexity. - Maria Refinetti; Alessandro Ingrosso; Sebastian Goldt (ICML, 2023)

Machine learning at the mesoscale: a computation-dissipation bottleneck. - Alessandro Ingrosso; Emanuele Panizon (CoRR, 2023)

Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers. - Federico Bassetti; Marco Gherardi; Alessandro Ingrosso; Mauro Pastore; Pietro Rotondo (CoRR, 2024)

How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks. - William T. Redman; Zhangyang Wang; Alessandro Ingrosso; Sebastian Goldt (CPAL, 2025)

Density of states in neural networks: an in-depth exploration of learning in parameter space. - Margherita Mele; Roberto Menichetti; Alessandro Ingrosso; Raffaello Potestio (Trans. Mach. Learn. Res., 2025)

Generalization performance of narrow one-hidden layer networks in the teacher-student setting. - Jean Barbier; Federica Gerace; Alessandro Ingrosso; Clarissa Lauditi; Enrico M. Malatesta; Gibbs Nwemadji; Rodrigo Pérez Ortiz (CoRR, 2025)