Search Results for author:
Found 25 papers, 4 papers with code
DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM
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.
SOFARI: High-Dimensional Manifold-Based Inference
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.
ARK: Robust Knockoffs Inference with Coupling
We investigate the robustness of the model-X knockoffs framework with respect to the misspecified or estimated feature distribution.
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset.
SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals
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.
FACT: High-Dimensional Random Forests Inference
Quantifying the usefulness of individual features in random forests learning can greatly enhance its interpretability.
Dimension-Free Average Treatment Effect Inference with Deep Neural Networks
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.
SDNet: mutil-branch for single image deraining using swin
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
SIMPLE: Statistical Inference on Membership Profiles in Large Networks
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
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.
Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors
The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation.
Classification with imperfect training labels
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.
RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs
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
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
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
Heterogeneity is often natural in many contemporary applications involving massive data.
Interaction Pursuit with Feature Screening and Selection
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.
Interaction pursuit in high-dimensional multi-response regression via distance correlation
Feature interactions can contribute to a large proportion of variation in many prediction models.
Tuning parameter selection in high dimensional penalized likelihood
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.
High dimensional thresholded regression and shrinkage effect
In this paper, we consider sparse regression with hard-thresholding penalty, which we show to give rise to thresholded regression.
Asymptotic equivalence of regularization methods in thresholded parameter space
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.
Asymptotic properties for combined $L_1$ and concave regularization
Two important goals of high-dimensional modeling are prediction and variable selection.
Innovated scalable efficient estimation in ultra-large Gaussian graphical models
Large-scale precision matrix estimation is of fundamental importance yet challenging in many contemporary applications for recovering Gaussian graphical models.
Innovated interaction screening for high-dimensional nonlinear classification
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.
Optimal classification in sparse Gaussian graphic model
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.
