Gal Vardi

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy

2023 · arXiv (Cornell University)

Understanding when neural networks can be learned efficiently is a fundamental question in learning theory. Existing hardness results suggest that assumptions on both the input distribution and the network's weights are necessary for obtaining efficient …

Noisy Interpolation Learning with Shallow Univariate ReLU Networks

2023 · arXiv (Cornell University)

Understanding how overparameterized neural networks generalize despite perfect interpolation of noisy training data is a fundamental question. Mallinar et. al. 2022 noted that neural networks seem to often exhibit ``tempered overfitting'', wherein the population risk …