154 citations · 861 across the 94 of their papers we have counts for
4 papers · 2 filters
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
Samet Oymak, Zalan Fabian, Mingchen Li +1
Modern neural network architectures often generalize well despite containing many more parameters than the size of the training dataset. This paper explores the generalization capa…
Quickly Finding the Best Linear Model in High Dimensions
Yahya Sattar, Samet Oymak
We study the problem of finding the best linear model that can minimize least-squares loss given a data-set. While this problem is trivial in the low dimensional regime, it becomes…
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak
Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks in pri…
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Samet Oymak, Mahdi Soltanolkotabi
Many modern neural network architectures are trained in an overparameterized regime where the parameters of the model exceed the size of the training dataset. Sufficiently overpara…