b'Noah Simon'

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Featured Co-authors

Li Liu
132 publications
Xu Wang
65 publications
Robert Tibshirani
38 publications
Ali Shojaie
37 publications
Ryan J. Tibshirani
35 publications
Trevor Hastie
33 publications
Tianyu Zhang
32 publications
Jacob Bien
31 publications
Jon Wakefield
29 publications
Daniela Witten
25 publications
Jonathan Taylor
21 publications

research

∙ 11/06/2022

A framework for leveraging machine learning tools to estimate personalized survival curves

The conditional survival function of a time-to-event outcome subject to ...

0 Charles J. Wolock, et al. ∙

research

∙ 06/24/2022

Accounting for Inconsistent Use of Covariate Adjustment in Group Sequential Trials

Group sequential designs are common in clinical trials, as they allow fo...

0 Marlena S. Bannick, et al. ∙

research

∙ 06/07/2022

Regression in Tensor Product Spaces by the Method of Sieves

Estimation of a conditional mean (linking a set of features to an outcom...

0 Tianyu Zhang, et al. ∙

research

∙ 12/07/2021

Mesh-Based Solutions for Nonparametric Penalized Regression

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

0 Brayan Ortiz, et al. ∙

research

∙ 07/13/2021

The Future will be Different than Today: Model Evaluation Considerations when Developing Translational Clinical Biomarker

Finding translational biomarkers stands center stage of the future of pe...

0 Yichen Lu, et al. ∙

research

∙ 05/05/2021

On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure

Originally developed for imputing missing entries in low rank, or approx...

0 Yunhua Xiang, et al. ∙

research

∙ 04/02/2021

A Sieve Stochastic Gradient Descent Estimator for Online Nonparametric Regression in Sobolev ellipsoids

The goal of regression is to recover an unknown underlying function that...

0 Tianyu Zhang, et al. ∙

research

∙ 04/01/2021

An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces

The goal of nonparametric regression is to recover an underlying regress...

0 Tianyu Zhang, et al. ∙

research

∙ 05/11/2020

Ensembled sparse-input hierarchical networks for high-dimensional datasets

Neural networks have seen limited use in prediction for high-dimensional...

0 Jean Feng, et al. ∙

research

∙ 04/11/2020

Spatial Matrix Completion for Spatially-Misaligned and High-Dimensional Air Pollution Data

In health-pollution cohort studies, accurate predictions of pollutant co...

0 Phuong T. Vu, et al. ∙

research

∙ 03/01/2020

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data

In order to implement disease-specific interventions in young age groups...

0 Austin E Schumacher, et al. ∙

research

∙ 02/28/2020

BigSurvSGD: Big Survival Data Analysis via Stochastic Gradient Descent

In many biomedical applications, outcome is measured as a “time-to-event...

0 Aliasghar Tarkhan, et al. ∙

research

∙ 12/28/2019

Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep

Successful deployment of machine learning algorithms in healthcare requi...

0 Jean Feng, et al. ∙

research

∙ 06/13/2019

Selective prediction-set models with coverage guarantees

Though black-box predictors are state-of-the-art for many complex tasks,...

0 Jean Feng, et al. ∙

research

∙ 05/29/2019

Using Propensity Scores to Develop and Evaluate Treatment Rules with Observational Data

In this paper, we outline a principled approach to estimate an individua...

0 Jeremy Roth, et al. ∙

research

∙ 03/29/2019

Estimation of cell lineage trees by maximum-likelihood phylogenetics

CRISPR technology has enabled large-scale cell lineage tracing for compl...

0 Jean Feng, et al. ∙

research

∙ 03/28/2019

An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression

In the regression setting, given a set of hyper-parameters, a model-esti...

0 Jean Feng, et al. ∙

research

∙ 03/11/2019

Generalized Sparse Additive Models

We present a unified framework for estimation and analysis of generalize...

0 Asad Haris, et al. ∙

research

∙ 03/11/2019

Wavelet regression and additive models for irregularly spaced data

We present a novel approach for nonparametric regression using wavelet b...

16 Asad Haris, et al. ∙

research

∙ 11/21/2017

Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification

Neural networks are usually not the tool of choice for nonparametric hig...

0 Jean Feng, et al. ∙

research

∙ 11/11/2017

Survival analysis of DNA mutation motifs with penalized proportional hazards

Antibodies, an essential part of our immune system, develop in an intric...

0 Jean Feng, et al. ∙

research

∙ 03/28/2017

Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions

In high-dimensional and/or non-parametric regression problems, regulariz...

0 Jean Feng, et al. ∙

research

∙ 10/13/2014

Convex Modeling of Interactions with Strong Heredity

We consider the task of fitting a regression model involving interaction...

0 Asad Haris, et al. ∙

research

∙ 09/18/2014

Fused Lasso Additive Model

We consider the problem of predicting an outcome variable using p covari...

0 Ashley Petersen, et al. ∙

research

∙ 05/16/2014

Selection Bias Correction and Effect Size Estimation under Dependence

We consider large-scale studies in which it is of interest to test a ver...

0 Kean Ming Tan, et al. ∙

research

∙ 11/26/2013

A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression

In this paper we purpose a blockwise descent algorithm for group-penaliz...

0 Noah Simon, et al. ∙

research

∙ 11/15/2013

On Estimating Many Means, Selection Bias, and the Bootstrap

With recent advances in high throughput technology, researchers often fi...

0 Noah Simon, et al. ∙

research

∙ 06/27/2012

A Permutation Approach to Testing Interactions in Many Dimensions

To date, testing interactions in high dimensions has been a challenging ...

0 Noah Simon, et al. ∙

research

∙ 11/07/2011

Discriminant Analysis with Adaptively Pooled Covariance

Linear and Quadratic Discriminant analysis (LDA/QDA) are common tools fo...

0 Noah Simon, et al. ∙

research

∙ 11/09/2010

Strong rules for discarding predictors in lasso-type problems

We consider rules for discarding predictors in lasso regression and rela...

0 Robert Tibshirani, et al. ∙