45 citations · 202 across the 24 of their papers we have counts for
6 papers · 1 filter
Adaptive Non-Parametric Regression With the -NN Fused Lasso
Oscar Hernan Madrid Padilla, James Sharpnack, Yanzhen Chen +1
The fused lasso, also known as total-variation denoising, is a locally-adaptive function estimator over a regular grid of design points. In this paper, we extend the fused lasso to…
Distributed Cartesian Power Graph Segmentation for Graphon Estimation
Shitong Wei, Oscar Hernan Madrid-Padilla, James Sharpnack
We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models. The power graph fuse…
Fused Density Estimation: Theory and Methods
Robert Bassett, James Sharpnack
In this paper we introduce a method for nonparametric density estimation on geometric networks. We define fused density estimators as solutions to a total variation regularized max…
SQL-Rank: A Listwise Approach to Collaborative Ranking
Liwei Wu, Cho-Jui Hsieh, James Sharpnack
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to pre…
Estimating Graphlet Statistics via Lifting
Kirill Paramonov, Dmitry Shemetov, James Sharpnack
Exploratory analysis over network data is often limited by the ability to efficiently calculate graph statistics, which can provide a model-free understanding of the macroscopic pr…
Learning Patterns for Detection with Multiscale Scan Statistics
James Sharpnack
This paper addresses detecting anomalous patterns in images, time-series, and tensor data when the location and scale of the pattern is unknown a priori. The multiscale scan statis…