no code implementations • ICML 2020 • Yunhua Xiang, Noah Simon

In addition, we show that for 2 of these measures there are simple, strong plug-in estimators that require only the estimation of a conditional mean.

no code implementations • 7 Dec 2021 • Brayan Ortiz, Noah Simon

It is often of interest to estimate regression functions non-parametrically.

no code implementations • 13 Jul 2021 • Yichen Lu, Jane Fridlyand, Tiffany Tang, Ting Qi, Noah Simon, Ning Leng

Finding translational biomarkers stands center stage of the future of personalized medicine in healthcare.

no code implementations • 5 May 2021 • Yunhua Xiang, Tianyu Zhang, Xu Wang, Ali Shojaie, Noah Simon

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 reason to assume low-dimensional linear structure in the underlying matrix, as would be imposed by rank constraints.

3 code implementations • 11 May 2020 • Jean Feng, Noah Simon

Neural networks have seen limited use in prediction for high-dimensional data with small sample sizes, because they tend to overfit and require tuning many more hyperparameters than existing off-the-shelf machine learning methods.

1 code implementation • 28 Dec 2019 • Jean Feng, Scott Emerson, Noah Simon

Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety.

1 code implementation • 13 Jun 2019 • Jean Feng, Arjun Sondhi, Jessica Perry, Noah Simon

Though black-box predictors are state-of-the-art for many complex tasks, they often fail to properly quantify predictive uncertainty and may provide inappropriate predictions for unfamiliar data.

no code implementations • 28 Mar 2019 • Jean Feng, Noah Simon

We establish that the fitted models are Lipschitz in the penalty parameters and thus our oracle inequalities apply.

no code implementations • NeurIPS 2018 • Asad Haris, Noah Simon, Ali Shojaie

We prove minimax optimal convergence rates under a weak compatibility condition for sparse additive models.

no code implementations • 11 Mar 2019 • Asad Haris, Noah Simon, Ali Shojaie

We present a unified framework for estimation and analysis of generalized additive models in high dimensions.

1 code implementation • ICML 2018 • Jean Feng, Brian Williamson, Noah Simon, Marco Carone

In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome.

1 code implementation • 21 Nov 2017 • Jean Feng, Noah Simon

In addition, we characterize the statistical convergence of the penalized empirical risk minimizer to the optimal neural network: we show that the excess risk of this penalized estimator only grows with the logarithm of the number of input features; and we show that the weights of irrelevant features converge to zero.

no code implementations • 28 Mar 2017 • Jean Feng, Noah Simon

It is more efficient to tune parameters if the gradient can be determined, but this is often difficult for problems with non-smooth penalty functions.

no code implementations • 30 Nov 2016 • Asad Haris, Ali Shojaie, Noah Simon

We further establish minimax rates for a large class of sparse additive models.

no code implementations • 13 Oct 2014 • Asad Haris, Daniela Witten, Noah Simon

We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity.

no code implementations • 18 Sep 2014 • Ashley Petersen, Daniela Witten, Noah Simon

We consider the problem of predicting an outcome variable using $p$ covariates that are measured on $n$ independent observations, in the setting in which flexible and interpretable fits are desirable.

no code implementations • 16 May 2014 • Kean Ming Tan, Noah Simon, Daniela Witten

Many authors have proposed methods to reduce the effects of selection bias under the assumption that the naive estimates of the effect sizes are independent.

no code implementations • 26 Nov 2013 • Noah Simon, Jerome Friedman, Trevor Hastie

In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression.

no code implementations • 15 Nov 2013 • Noah Simon, Richard Simon

With recent advances in high throughput technology, researchers often find themselves running a large number of hypothesis tests (thousands+) and esti- mating a large number of effect-sizes.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.