Gal Mishne

Learning Sample Reweighting for Accuracy and Adversarial Robustness

2022 · arXiv (Cornell University)

There has been great interest in enhancing the robustness of neural network classifiers to defend against adversarial perturbations through adversarial training, while balancing the trade-off between robust accuracy and standard accuracy. We propose a novel …

Graph Laplacian Learning with Exponential Family Noise

2023 · arXiv (Cornell University)

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph …