Timezone: »

 
Poster
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Valentin De Bortoli · Alain Durmus · Xavier Fontaine · Umut Simsekli

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #391
in Poster Session 2 »
In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neural networks, as the number or neurons (i.e., the size of the hidden layer) $N \to \plusinfty$. Following a probabilistic approach, we show `propagation of chaos' for the particle system defined by this continuous-time dynamics under different scenarios, indicating that the statistical interaction between the particles asymptotically vanishes. In particular, we establish quantitative convergence with respect to $N$ of any particle to a solution of a mean-field McKean-Vlasov equation in the metric space endowed with the Wasserstein distance. In comparison to previous works on the subject, we consider settings in which the sequence of stepsizes in SGD can potentially depend on the number of neurons and the iterations. We then identify two regimes under which different mean-field limits are obtained, one of them corresponding to an implicitly regularized version of the minimization problem at hand. We perform various experiments on real datasets to validate our theoretical results, assessing the existence of these two regimes on classification problems and illustrating our convergence results.

Author Information

Valentin De Bortoli (ENS Paris-Saclay)
Alain Durmus (ENS Paris Saclay)
Xavier Fontaine (ENS Paris-Saclay)
Umut Simsekli (Inria/ENS)

More from the Same Authors