Publications by Francesco Spadaro

Lattice Models and Conformal Field Theory

Clément Hongler, Francesco Spadaro, Franck Raymond Gabriel

This book introduces the mathematical ideas connecting Statistical Mechanics and Conformal Field Theory (CFT). Building advanced structures on top of more elementary ones, the authors map out a well-posed road from simple lattice models to CFTs. Structure ...

Courant Institute of Mathematical Sciences2024

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

Complex Analysis meets Statistical Mechanics: Applications to Kernel Methods and Conformal Field Theory

Francesco Spadaro

This thesis is centered on questions coming from Machine Learning (ML) and Statistical Field Theory (SFT). In Machine Learning, we consider the subfield of Supervised Learning (SL), and in particular regression tasks where one tries to find a regressor tha ...

EPFL2021

Asymptotic symmetry and asymptotic solutions to Ito stochastic differential equations

Francesco Spadaro

We consider several aspects of conjugating symmetry methods, including the method of invariants, with an asymptotic approach. In particular we consider how to extend to the stochastic setting several ideas which are well established in the deterministic on ...

AMER INST MATHEMATICAL SCIENCES-AIMS2021

Implicit Regularization of Random Feature Models

Clément Hongler, Francesco Spadaro, Franck Raymond Gabriel, Berfin Simsek, Arthur Ulysse Jacot-Guillarmod

Random Feature (RF) models are used as efficient parametric approximations of kernel methods. We investigate, by means of random matrix theory, the connection between Gaussian RF models and Kernel Ridge Regression (KRR). For a Gaussian RF model with P feat ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020