research
∙
02/19/2023
Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep learning
We present a methodology for using unlabeled data to design semi supervi...
share
research
∙
12/17/2022
Inference with approximate local false discovery rates
Efron's two-group model is widely used in large scale multiple testing. ...
research
∙
06/07/2022
Integrating Random Effects in Deep Neural Networks
Modern approaches to supervised learning like deep neural networks (DNNs...
research
∙
05/04/2021
Resampling Methods for Detecting Anisotropic Correlation Structure
This paper proposes parametric and non-parametric hypothesis testing alg...
research
∙
04/03/2021
Optimal multiple testing and design in clinical trials
A central goal in designing clinical trials is to find the test that max...
research
∙
02/16/2021
Trees-Based Models for Correlated Data
This paper presents a new approach for trees-based regression, such as s...
research
∙
09/01/2020
Semi-Supervised Empirical Risk Minimization: When can unlabeled data improve prediction
We present a general methodology for using unlabeled data to design semi...
research
∙
04/04/2019
Cross-Validation for Correlated Data
K-fold cross-validation (CV) with squared error loss is widely used for ...
research
∙
03/19/2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Interpolators -- estimators that achieve zero training error -- have att...
research
∙
02/03/2019
Optimal FDR control in the two-group model
The highly influential two group model in testing a large number of stat...
research
∙
01/25/2019
Rescaling and other forms of unsupervised preprocessing introduce bias into cross-validation
Cross-validation of predictive models is the de-facto standard for model...
research
∙
12/10/2018
Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models
We consider the problem of predicting several response variables using t...
research
∙
11/25/2018
Innovation Representation of Stochastic Processes with Application to Causal Inference
Typically, real-world stochastic processes are not easy to analyze. In t...
research
∙
10/26/2018
Lossless (and Lossy) Compression of Random Forests
Ensemble methods are among the state-of-the-art predictive modeling appr...
research
∙
09/16/2018
Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds
Independent Component Analysis (ICA) is a statistical tool that decompos...
research
∙
04/26/2018
Optimal Procedures for Multiple Testing Problems
Multiple testing problems are a staple of modern statistical analysis. T...
research
∙
03/12/2018
The Everlasting Database: Statistical Validity at a Fair Price
The problem of handling adaptivity in data analysis, intentional or not,...
research
∙
02/03/2018
Assessing Prediction Error at Interpolation and Extrapolation Points
Common model selection criteria, such as AIC and its variants, are based...
research
∙
01/11/2018
Modeling High-Dimensional Data with Case-Control Sampling and Dependency Structures
Modern data sets in various domains often include units that were sample...
research
∙
12/10/2015
Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance
Recursive partitioning approaches producing tree-like models are a long ...
research
∙
11/12/2013
When Does More Regularization Imply Fewer Degrees of Freedom? Sufficient Conditions and Counter Examples from Lasso and Ridge Regression
Regularization aims to improve prediction performance of a given statist...
research
∙
08/16/2007