18 citations · 23 across the 8 of their papers we have counts for
8 papers
Decomposing multimodal embedding spaces with group-sparse autoencoders
Chiraag Kaushik, Davis Barch, Andrea Fanelli
The Linear Representation Hypothesis asserts that the embeddings learned by neural networks can be understood as linear combinations of features corresponding to high-level concept…
A general technique for approximating high-dimensional empirical kernel matrices
Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
We present simple, user-friendly bounds for the expected operator norm of a random kernel matrix under general conditions on the kernel function . Our approach uses…
MANGO: Learning Disentangled Image Transformation Manifolds with Grouped Operators
Brighton Ancelin, Yenho Chen, Peimeng Guan +4
Learning semantically meaningful image transformations (i.e. rotation, thickness, blur) directly from examples can be a challenging task. Recently, the Manifold Autoencoder (MAE) p…
Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares pr…
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Chiraag Kaushik, Ran Liu, Chi-Heng Lin +5
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is wide…
New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport +1
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…
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer +1
Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are tr…
Network topology change-point detection from graph signals with prior spectral signatures
Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra
We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying g…