2 papers
stat.ML2026
Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks
Katerina Papagiannouli, Dario Trevisan, Giuseppe Pio Zitto
We study wide Bayesian neural networks focusing on the rare but statistically dominant fluctuations that govern posterior concentration, beyond Gaussian-process limits. Large-devia…
stat.ML2025
Quantitative convergence of trained single layer neural networks to Gaussian processes
Eloy Mosig, Andrea Agazzi, Dario Trevisan
In this paper, we study the quantitative convergence of shallow neural networks trained via gradient descent to their associated Gaussian processes in the infinite-width limit. Whi…