activity
20092015
most citedMathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions

15 citations · 27 across the 5 of their papers we have counts for

collaborators

9 papers

stat.ML2015★ 15 cited

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions

Andrew S. Lan, Divyanshu Vats, Andrew E. Waters +1

While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assign…

stat.ML2014★ 2 cited

Active Learning for Undirected Graphical Model Selection

Divyanshu Vats, Robert D. Nowak, Richard G. Baraniuk

This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical mo…

math.ST2014★ 2 cited

Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression

Divyanshu Vats, Richard G. Baraniuk

In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, c…

math.ST2013★ 5 cited

Swapping Variables for High-Dimensional Sparse Regression with Correlated Measurements

Divyanshu Vats, Richard G. Baraniuk

We consider the high-dimensional sparse linear regression problem of accurately estimating a sparse vector using a small number of linear measurements that are contaminated by nois…

stat.ML2013

A Junction Tree Framework for Undirected Graphical Model Selection

Divyanshu Vats, Robert Nowak

An undirected graphical model is a joint probability distribution defined on an undirected graph G*, where the vertices in the graph index a collection of random variables and the…

stat.ML2012

High-Dimensional Screening Using Multiple Grouping of Variables

Divyanshu Vats

Screening is the problem of finding a superset of the set of non-zero entries in an unknown p-dimensional vector β* given n noisy observations. Naturally, we want this superset to…