Systems | Information | Learning | Optimization
 

Over-parametrized Matrix Estimation

Abstract: Modern machine learning systems commonly use over-parametrized models, where the number of parameters exceeds the sample size or intrinsic dimension, with no explicit regularization. Existing theory often fails to explain their good performance (or the lack thereof). In this talk, we present two recent results on understanding over-parametrization in the context of nonconvex formulations for matrix estimation.

We first consider grossly over-parametrized matrix estimation with a quadratic loss, for which all global optima provably overfit noisy data. We show that with early stopping, gradient descent from a small random initialization converges to the best rank-r approximation of a general matrix (low-rank or not) at a linear rate. This result highlights the implicit regularization effect of early stopping as well as the connection between random initialization and spectral learning.

We next consider a non-smooth formulation for recovering a low-rank matrix from linear measurements with adversarial corruption. We show that subgradient method equipped with an adaptive stepsize converges at a sublinear rate with over-parametrization and at a linear rate with exact-parametrization, whereas standard stepsize rules fail to perform well in both settings. We further demonstrate that with small initialization, our method regains linear convergence even under over-parametrization.

Bio: Yudong Chen is a faculty member of the Computer Sciences Department, University of Wisconsin-Madison. Before joining UW-Madison, he was an associate professor at the School of Operations Research and Information Engineering, Cornell University, and a postdoctoral scholar at University of California, Berkeley. He obtained his Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin. His research interests include machine learning, high-dimensional statistics, convex and nonconvex optimization, and reinforcement learning.

November 24 @ 12:30
12:30 pm (1h)

Orchard View Room, Virtual

Yudong Chen