62 citations · 315 across the 28 of their papers we have counts for
5 papers · 1 filter
On the Approximation Properties of Random ReLU Features
Yitong Sun, Anna Gilbert, Ambuj Tewari
We study the approximation properties of random ReLU features through their reproducing kernel Hilbert space (RKHS). We first prove a universality theorem for the RKHS induced by r…
But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun, Anna Gilbert, Ambuj Tewari
We prove that, under low noise assumptions, the support vector machine with random features (RFSVM) can achieve the learning rate faster than on a training…
Generalized Metric Repair on Graphs
Anna C. Gilbert, Rishi Sonthalia
Many modern data analysis algorithms either assume that or are considerably more efficient if the distances between the data points satisfy a metric. These algorithms include metri…
Unsupervised Metric Learning in Presence of Missing Data
Anna C. Gilbert, Rishi Sonthalia
For many machine learning tasks, the input data lie on a low-dimensional manifold embedded in a high dimensional space and, because of this high-dimensional structure, most algorit…
Property Testing for Differential Privacy
Anna Gilbert, Audra McMillan
We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular,…