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07/10/2023
ARK: Robust Knockoffs Inference with Coupling
We investigate the robustness of the model-X knockoffs framework with re...
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10/31/2022
SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals
Large-scale network inference with uncertainty quantification has import...
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07/04/2022
FACT: High-Dimensional Random Forests Inference
Random forests is one of the most widely used machine learning methods o...
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12/18/2021
High-Dimensional Knockoffs Inference for Time Series Data
The framework of model-X knockoffs provides a flexible tool for exact fi...
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12/02/2021
Dimension-Free Average Treatment Effect Inference with Deep Neural Networks
This paper investigates the estimation and inference of the average trea...
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04/29/2020
Asymptotic Properties of High-Dimensional Random Forests
As a flexible nonparametric learning tool, random forest has been widely...
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10/28/2019
Asymptotic Distributions of High-Dimensional Nonparametric Inference with Distance Correlation
Understanding the nonlinear association between a pair of potentially hi...
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10/03/2019
SIMPLE: Statistical Inference on Membership Profiles in Large Networks
Network data is prevalent in many contemporary big data applications in ...
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02/19/2019
Asymptotic Theory of Eigenvectors for Large Random Matrices
Characterizing the exact asymptotic distributions of high-dimensional ei...
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09/06/2018
IPAD: Stable Interpretable Forecasting with Knockoffs Inference
Interpretability and stability are two important features that are desir...
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09/04/2018
DeepPINK: reproducible feature selection in deep neural networks
Deep learning has become increasingly popular in both supervised and uns...
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08/25/2018
DNN: A Two-Scale Distributional Tale of Heterogeneous Treatment Effect Inference
Heterogeneous treatment effects are the center of gravity in many modern...
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03/17/2018
Large-Scale Model Selection with Misspecification
Model selection is crucial to high-dimensional learning and inference fo...
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10/07/2017
Nonsparse learning with latent variables
As a popular tool for producing meaningful and interpretable models, lar...
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08/31/2017
RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs
Power and reproducibility are key to enabling refined scientific discove...
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04/26/2017
SOFAR: large-scale association network learning
Many modern big data applications feature large scale in both numbers of...
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06/13/2016
Tuning-Free Heterogeneity Pursuit in Massive Networks
Heterogeneity is often natural in many contemporary applications involvi...
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05/28/2016
Interaction Pursuit with Feature Screening and Selection
Understanding how features interact with each other is of paramount impo...
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05/11/2016
Asymptotic properties for combined L_1 and concave regularization
Two important goals of high-dimensional modeling are prediction and vari...
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05/11/2016
Interaction pursuit in high-dimensional multi-response regression via distance correlation
Feature interactions can contribute to a large proportion of variation i...
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05/11/2016
Innovated scalable efficient estimation in ultra-large Gaussian graphical models
Large-scale precision matrix estimation is of fundamental importance yet...
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05/11/2016
The constrained Dantzig selector with enhanced consistency
The Dantzig selector has received popularity for many applications such ...
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05/11/2016
Asymptotic equivalence of regularization methods in thresholded parameter space
High-dimensional data analysis has motivated a spectrum of regularizatio...
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05/11/2016
High dimensional thresholded regression and shrinkage effect
High-dimensional sparse modeling via regularization provides a powerful ...
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12/23/2014