20 citations · 42 across the 12 of their papers we have counts for
12 papers
GAN-based Vertical Federated Learning for Label Protection in Binary Classification
Yujin Han, Leying Guan
Split learning (splitNN) has emerged as a popular strategy for addressing the high computational costs and low modeling efficiency in Vertical Federated Learning (VFL). However, de…
Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection
Yujin Han, Mingwenchan Xu, Leying Guan
The Random Forests classifier, a widely utilized off-the-shelf classification tool, assumes training and test samples come from the same distribution as other standard classifiers.…
-norm constrained multi-block sparse canonical correlation analysis via proximal gradient descent
Leying Guan
Multi-block CCA constructs linear relationships explaining coherent variations across multiple blocks of data. We view the multi-block CCA problem as finding leading generalized ei…
Localized Conformal Prediction: A Generalized Inference Framework for Conformal Prediction
Leying Guan
We propose a new inference framework called localized conformal prediction. It generalizes the framework of conformal prediction by offering a single-test-sample adaptive construct…
Smooth and probabilistic PARAFAC model with auxiliary covariates
Leying Guan
In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular. Researchers are interested in finding low-dimensional embedding of subject…
Conformal prediction with localization
Leying Guan
We propose a new method called localized conformal prediction, where we can perform conformal inference using only a local region around a new test sample to construct its confiden…
Prediction and outlier detection in classification problems
Leying Guan, Rob Tibshirani
We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanc…
Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control
Leying Guan, Xi Chen, Wing Hung Wong
The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To in…
Post model-fitting exploration via a "Next-Door" analysis
Leying Guan, Robert Tibshirani
We propose a simple method for evaluating the model that has been chosen by an adaptive regression procedure, our main focus being the lasso. This procedure deletes each chosen pre…
Test Error Estimation after Model Selection Using Validation Error
Leying Guan
When performing supervised learning with the model selected using validation error from sample splitting and cross validation, the minimum value of the validation error can be bias…
Approximate -penalized estimation of piecewise-constant signals on graphs
Zhou Fan, Leying Guan
We study recovery of piecewise-constant signals on graphs by the estimator minimizing an -edge-penalized objective. Although exact minimization of this objective may be comput…
Regularization for supervised learning via the "hubNet" procedure
Leying Guan, Zhou Fan, Robert Tibshirani
We propose a new method for supervised learning. The hubNet procedure fits a hub-based graphical model to the predictors, to estimate the amount of "connection" that each predictor…