4 accepted papers
We provide a rigorous analysis of implicit regularization in an overparametrized tensor factorization problem beyond the lazy training regime. For matrix factorization problems, this phenomenon has been studied in a number of works. A particular challenge has been to design universal initialization…
A common problem in recommendation systems is to learn a model of user preferences based only on comparisons of the relative attractiveness of different items. We consider this problem in the context of an ideal point model of user preference, where each user can be represented as a point in a low-d…
Overparametrized neural networks tend to perfectly fit noisy training data yet generalize well on test data. Inspired by this empirical observation, recent work has sought to understand this phenomenon of benign overfitting or harmless interpolation in the much simpler linear model. Previous theoret…
In this paper, we provide a dictionary for representing the discrete vector one obtains when collecting a finite set of uniform samples from a baseband analog signal. Like the discrete prolate spheroidal sequences (DPSS's), the proposed orthogonal basis compactly captures most of the energy in overs…