no code implementations • 18 Feb 2024 • Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, Eva L Dyer
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance.
no code implementations • 3 May 2023 • Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick.
no code implementations • 10 Oct 2022 • Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar
Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning.
no code implementations • 21 Oct 2020 • Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra
We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem.