458 citations · 893 across the 37 of their papers we have counts for
7 papers · 1 filter
Semi-supervised sequence classification through change point detection
Nauman Ahad, Mark A. Davenport
Sequential sensor data is generated in a wide variety of practical applications. A fundamental challenge involves learning effective classifiers for such sequential data. While dee…
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…
Generative causal explanations of black-box classifiers
Matthew O'Shaughnessy, Gregory Canal, Marissa Connor +2
We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data. The explanation is causal in…
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…
Improved bounds for the eigenvalues of prolate spheroidal wave functions and discrete prolate spheroidal sequences
Santhosh Karnik, Justin Romberg, Mark A. Davenport
The discrete prolate spheroidal sequences (DPSSs) are a set of orthonormal sequences in which are strictly bandlimited to a frequency band and maximal…
Dynamic Knowledge embedding and tracing
Liangbei Xu, Mark A. Davenport
The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a k…
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…