Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborov√°


We analyze in a closed form the learning dynamics of stochastic gradient descent (SGD) for a single layer neural network classifying a high-dimensional Gaussian mixture where each cluster is assigned one of two labels. This problem provides a prototype of a non-convex loss landscape with interpolating regimes and a large generalization gap. We define a particular stochastic process for which SGD can be extended to a continuous-time limit that we call stochastic gradient flow. In the full-batch limit we recover the standard gradient flow. We apply dynamical mean-field theory from statistical physics to track the dynamics of the algorithm in the high-dimensional limit via a self-consistent stochastic process. We explore the performance of the algorithm as a function of control parameters shedding light on how it navigates the loss landscape.