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cs.LG2026
Quantitative Gaussian-Process limits of Tensor Programs
Andrea Agazzi, Eloy Mosig García, Dario Trevisan
We study the infinite-width Gaussian-process limit of random neural networks through the lens of tensor programs, and we provide a quantitative convergence theory in Wasserstein di…
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…