Pranjal Awasthi

Estimating Principal Components under Adversarial Perturbations.

2020

Robustness is a key requirement for widespread deployment of machine learning algorithms, and has received much attention in both statistics and computer science. We study a natural model of robustness for high-dimensional statistical estimation problems …

Adversarially Robust Low Dimensional Representations

2021 · arXiv (Cornell University)

Many machine learning systems are vulnerable to small perturbations made to inputs either at test time or at training time. This has received much recent interest on the empirical front due to applications where reliability …

Adversarial Robustness Across Representation Spaces

2021

Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial …

Beyond GNNs: An Efficient Architecture for Graph Problems

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Despite their popularity for graph structured data, existing Graph Neural Networks (GNNs) have inherent limitations for fundamental graph problems such as shortest paths, k-connectivity, minimum spanning tree and minimum cuts. In these instances, it is …