Search Results for author: Jinchi Lv

Found 22 papers, 2 papers with code

SOFARI: High-Dimensional Manifold-Based Inference

no code implementations26 Sep 2023 Zemin Zheng, Xin Zhou, Yingying Fan, Jinchi Lv

In this paper, we suggest a novel approach called high-dimensional manifold-based SOFAR inference (SOFARI), drawing on the Neyman near-orthogonality inference while incorporating the Stiefel manifold structure imposed by the SVD constraints.

Multi-Task Learning

ARK: Robust Knockoffs Inference with Coupling

no code implementations10 Jul 2023 Yingying Fan, Lan Gao, Jinchi Lv

We investigate the robustness of the model-X knockoffs framework with respect to the misspecified or estimated feature distribution.

feature selection

SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals

no code implementations31 Oct 2022 Jianqing Fan, Yingying Fan, Jinchi Lv, Fan Yang

To address these practical challenges, in this paper we propose a SIMPLE method with random coupling (SIMPLE-RC) for testing the non-sharp null hypothesis that a group of given nodes share similar (not necessarily identical) membership profiles under weaker signals.

Uncertainty Quantification

FACT: High-Dimensional Random Forests Inference

no code implementations4 Jul 2022 Chien-Ming Chi, Yingying Fan, Jinchi Lv

Quantifying the usefulness of individual features in random forests learning can greatly enhance its interpretability.

Feature Importance Vocal Bursts Intensity Prediction

Dimension-Free Average Treatment Effect Inference with Deep Neural Networks

no code implementations2 Dec 2021 Xinze Du, Yingying Fan, Jinchi Lv, Tianshu Sun, Patrick Vossler

Under some regularity conditions, the observed response can be formulated as the response of a mean regression problem with both the confounding variables and the treatment indicator as the independent variables.

regression Uncertainty Quantification

SIMPLE: Statistical Inference on Membership Profiles in Large Networks

no code implementations3 Oct 2019 Jianqing Fan, Yingying Fan, Xiao Han, Jinchi Lv

Both tests are of the Hotelling-type statistics based on the rows of empirical eigenvectors or their ratios, whose asymptotic covariance matrices are very challenging to derive and estimate.

DeepPINK: reproducible feature selection in deep neural networks

1 code implementation NeurIPS 2018 Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble

In this paper, we describe a method to increase the interpretability and reproducibility of DNNs by incorporating the idea of feature selection with controlled error rate.

feature selection

Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors

no code implementations25 Aug 2018 Emre Demirkaya, Yingying Fan, Lan Gao, Jinchi Lv, Patrick Vossler, Jingbo Wang

The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation.

Causal Inference regression +1

Large-Scale Model Selection with Misspecification

no code implementations17 Mar 2018 Emre Demirkaya, Yang Feng, Pallavi Basu, Jinchi Lv

Our new information criterion characterizes the impacts of both model misspecification and high dimensionality on model selection.

Model Selection

Nonsparse learning with latent variables

no code implementations7 Oct 2017 Zemin Zheng, Jinchi Lv, Wei. Lin

A new methodology of nonsparse learning with latent variables (NSL) is proposed to simultaneously recover the significant observable predictors and latent factors as well as their effects.

Model Selection Sparse Learning

RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs

no code implementations31 Aug 2017 Yingying Fan, Emre Demirkaya, Gaorong Li, Jinchi Lv

We provide theoretical justifications on the robustness of our modified procedure by showing that the false discovery rate (FDR) is asymptotically controlled at the target level and the power is asymptotically one with the estimated covariate distribution.

SOFAR: large-scale association network learning

no code implementations26 Apr 2017 Yoshimasa Uematsu, Yingying Fan, Kun Chen, Jinchi Lv, Wei. Lin

Many modern big data applications feature large scale in both numbers of responses and predictors.

Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection

3 code implementations7 Oct 2016 Emmanuel Candes, Yingying Fan, Lucas Janson, Jinchi Lv

Whereas the knockoffs procedure is constrained to homoscedastic linear models with $n\ge p$, the key innovation here is that model-X knockoffs provide valid inference from finite samples in settings in which the conditional distribution of the response is arbitrary and completely unknown.

Methodology Statistics Theory Applications Statistics Theory

Tuning-Free Heterogeneity Pursuit in Massive Networks

no code implementations13 Jun 2016 Zhao Ren, Yongjian Kang, Yingying Fan, Jinchi Lv

Heterogeneity is often natural in many contemporary applications involving massive data.

Interaction Pursuit with Feature Screening and Selection

no code implementations28 May 2016 Yingying Fan, Yinfei Kong, Daoji Li, Jinchi Lv

The suggested method first reduces the number of interactions and main effects to a moderate scale by a new feature screening approach, and then selects important interactions and main effects in the reduced feature space using regularization methods.

High dimensional thresholded regression and shrinkage effect

no code implementations11 May 2016 Zemin Zheng, Yingying Fan, Jinchi Lv

In this paper, we consider sparse regression with hard-thresholding penalty, which we show to give rise to thresholded regression.

regression Variable Selection +1

Asymptotic equivalence of regularization methods in thresholded parameter space

no code implementations11 May 2016 Yingying Fan, Jinchi Lv

To assess their performance, we establish the oracle inequalities, as in Bickel, Ritov and Tsybakov (2009), of the global minimizer for these methods under various prediction and variable selection losses.

Variable Selection

Innovated scalable efficient estimation in ultra-large Gaussian graphical models

no code implementations11 May 2016 Yingying Fan, Jinchi Lv

Large-scale precision matrix estimation is of fundamental importance yet challenging in many contemporary applications for recovering Gaussian graphical models.

Asymptotic properties for combined $L_1$ and concave regularization

no code implementations11 May 2016 Yingying Fan, Jinchi Lv

Two important goals of high-dimensional modeling are prediction and variable selection.

Variable Selection

The constrained Dantzig selector with enhanced consistency

no code implementations11 May 2016 Yinfei Kong, Zemin Zheng, Jinchi Lv

An important question is whether this factor can be reduced to a logarithmic factor of the sample size in ultra-high dimensions under mild regularity conditions.

Computational Efficiency

Model Selection in High-Dimensional Misspecified Models

no code implementations23 Dec 2014 Pallavi Basu, Yang Feng, Jinchi Lv

Model selection is indispensable to high-dimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models.

Model Selection Vocal Bursts Intensity Prediction

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