1 code implementation • 5 Apr 2024 • Wenxuan Zuo, Zifan Zhu, Yuxuan Du, Yi-Chun Yeh, Jed A. Fuhrman, Jinchi Lv, Yingying Fan, Fengzhu Sun
DeepLINK-T combines deep learning with knockoff inference to control FDR in feature selection for time series models, accommodating a wide variety of feature distributions.
no code implementations • 26 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.
no code implementations • 10 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.
no code implementations • 15 Jun 2023 • Junru Chen, Yang Yang, Tao Yu, Yingying Fan, Xiaolong Mo, Carl Yang
Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset.
no code implementations • 31 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.
no code implementations • 4 Jul 2022 • Chien-Ming Chi, Yingying Fan, Jinchi Lv
Quantifying the usefulness of individual features in random forests learning can greatly enhance its interpretability.
no code implementations • 2 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.
3 code implementations • 31 May 2021 • Fuxiang Tan, YuTing Kong, Yingying Fan, Feng Liu, Daxin Zhou, Hao Zhang, Long Chen, Liang Gao, Yurong Qian
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
no code implementations • 3 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.
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.
no code implementations • 25 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.
no code implementations • 29 May 2018 • Timothy I. Cannings, Yingying Fan, Richard J. Samworth
One consequence of these results is that the knn and SVM classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risks of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods.
no code implementations • 31 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.
no code implementations • 26 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.
3 code implementations • 7 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
no code implementations • 13 Jun 2016 • Zhao Ren, Yongjian Kang, Yingying Fan, Jinchi Lv
Heterogeneity is often natural in many contemporary applications involving massive data.
no code implementations • 28 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.
no code implementations • 11 May 2016 • Yinfei Kong, Daoji Li, Yingying Fan, Jinchi Lv
Feature interactions can contribute to a large proportion of variation in many prediction models.
no code implementations • 11 May 2016 • Yingying Fan, Jinchi Lv
Two important goals of high-dimensional modeling are prediction and variable selection.
no code implementations • 11 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.
no code implementations • 11 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.
no code implementations • 11 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.
no code implementations • 11 May 2016 • Yingying Fan, Cheng Yong Tang
We examine this problem in the setting of penalized likelihood methods for generalized linear models, where the dimensionality of covariates p is allowed to increase exponentially with the sample size n. We propose to select the tuning parameter by optimizing the generalized information criterion (GIC) with an appropriate model complexity penalty.
no code implementations • 5 Jan 2015 • Yingying Fan, Yinfei Kong, Daoji Li, Zemin Zheng
We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is proposed, and in the second step a sparse quadratic discriminant analysis (SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification.
no code implementations • 21 Dec 2012 • Yingying Fan, Jiashun Jin, Zhigang Yao
We propose a two-stage classification method where we first select features by the method of Innovated Thresholding (IT), and then use the retained features and Fisher's LDA for classification.