74 citations · 461 across the 38 of their papers we have counts for
6 papers · 1 filter
Improved convergence rates of nonparametric penalized regression under misspecified total variation
Marlena S. Bannick, Noah Simon
Penalties that induce smoothness are common in nonparametric regression. In many settings, the amount of smoothness in the data generating function will not be known. Simon and Sho…
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Yunhua Xiang, Tianyu Zhang, Xu Wang +2
Originally developed for imputing missing entries in low rank, or approximately low rank matrices, matrix completion has proven widely effective in many problems where there is no…
A Sieve Stochastic Gradient Descent Estimator for Online Nonparametric Regression in Sobolev ellipsoids
Tianyu Zhang, Noah Simon
The goal of regression is to recover an unknown underlying function that best links a set of predictors to an outcome from noisy observations. In nonparametric regression, one assu…
BigSurvSGD: Big Survival Data Analysis via Stochastic Gradient Descent
Aliasghar Tarkhan, Noah Simon
In many biomedical applications, outcome is measured as a ``time-to-event'' (eg. disease progression or death). To assess the connection between features of a patient and this outc…
Graphical Models for Discrete and Continuous Data
Rui Zhuang, Noah Simon, Johannes Lederer
We introduce a general framework for undirected graphical models. It generalizes Gaussian graphical models to a wide range of continuous, discrete, and combinations of different ty…
Strong rules for discarding predictors in lasso-type problems
Robert Tibshirani, Jacob Bien, Jerome Friedman +4
We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency. El Ghaoui et al (2010) propose "SAFE" rules that guarantee that…