7 citations · 22 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
Simultaneous Preference and Metric Learning from Paired Comparisons
Austin Xu, Mark A. Davenport
A popular model of preference in the context of recommendation systems is the so-called \emph{ideal point} model. In this model, a user is represented as a vector toge…
Sample complexity and effective dimension for regression on manifolds
Andrew McRae, Justin Romberg, Mark Davenport
We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and…
Localized sketching for matrix multiplication and ridge regression
Rakshith S Srinivasa, Mark A Davenport, Justin Romberg
We consider sketched approximate matrix multiplication and ridge regression in the novel setting of localized sketching, where at any given point, only part of the data matrix is a…
Active embedding search via noisy paired comparisons
Gregory H. Canal, Andrew K. Massimino, Mark A. Davenport +1
Suppose that we wish to estimate a user's preference vector from paired comparisons of the form "does user prefer item or item ?," where both the user and items are…