15 citations · 49 across the 18 of their papers we have counts for
19 papers
AI-Augmented Statistical Network Estimation with Proxy Gene Embeddings
Yan Chen, Weijing Tang, Jin-Hong Du
Gene--gene networks are often observed only on a restricted target set, while modern biomedical foundation models provide proxy gene embeddings over substantially larger gene unive…
Beyond Importance: Interchange-Sobol Sensitivity Reveals Task-Specific Content Channels in Transformer Components
Yifeng Guo, Jin-Hong Du, Xiang Chen
Mechanistic interpretability methods summarize a transformer component by a single importance score, conflating two distinct roles: a component may matter because it transports tas…
Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
Common practice in modern machine learning involves fitting a large number of parameters relative to the number of observations. These overparameterized models can exhibit surprisi…
Precise Asymptotics of Bagging Regularized M-estimators
Takuya Koriyama, Pratik Patil, Jin-Hong Du +2
We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent…
Implicit Regularization Paths of Weighted Neural Representations
Jin-Hong Du, Pratik Patil
We study the implicit regularization effects induced by (observation) weighting of pretrained features. For weight and feature matrices of bounded operator norms that are infinites…
Distance-Preserving Representations for Genomic Spatial Reconstruction
Wenbin Zhou, Jin-Hong Du
The spatial context of single-cell gene expression data is crucial for many downstream analyses, yet often remains inaccessible due to practical and technical limitations, restrict…
Network-based Neighborhood regression
Yaoming Zhen, Jin-Hong Du
Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Cur…
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy +2
With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-c…
Optimal Ridge Regularization for Out-of-Distribution Prediction
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
We study the behavior of optimal ridge regularization and optimal ridge risk for out-of-distribution prediction, where the test distribution deviates arbitrarily from the train dis…
Augmented Doubly Robust Post-Imputation Inference for Proteomic Data
Haeun Moon, Jin-Hong Du, Jing Lei +1
Quantitative measurements produced by mass spectrometry proteomics experiments offer a direct way to explore the role of proteins in molecular mechanisms. However, analysis of such…
Corrected generalized cross-validation for finite ensembles of penalized estimators
Pierre C. Bellec, Jin-Hong Du, Takuya Koriyama +2
Generalized cross-validation (GCV) is a widely-used method for estimating the squared out-of-sample prediction risk that employs a scalar degrees of freedom adjustment (in a multip…
Simultaneous inference for generalized linear models with unmeasured confounders
Jin-Hong Du, Larry Wasserman, Kathryn Roeder
Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, ma…
Generalized equivalences between subsampling and ridge regularization
Pratik Patil, Jin-Hong Du
We establish precise structural and risk equivalences between subsampling and ridge regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic f…
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Jin-Hong Du, Pratik Patil, Arun Kumar Kuchibhotla
We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges…
Extrapolated cross-validation for randomized ensembles
Jin-Hong Du, Pratik Patil, Kathryn Roeder +1
Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of en…
Bagging in overparameterized learning: Risk characterization and risk monotonization
Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the performance of prediction procedures. In this paper, we study the prediction risk of…
High Dimensional Portfolio Selection with Cardinality Constraints
Jin-Hong Du, Yifeng Guo, Xueqin Wang
The expanding number of assets offers more opportunities for investors but poses new challenges for modern portfolio management (PM). As a central plank of PM, portfolio selection…
Fiber product homotopy method for multiparameter eigenvalue problems
Jose Israel Rodriguez, Jin-Hong Du, Yiling You +1
We develop a new homotopy method for solving multiparameter eigenvalue problems (MEPs) called the fiber product homotopy method. For a -parameter eigenvalue problem with matrice…
A Machine Learning Framework for Stock Selection
XingYu Fu, JinHong Du, YiFeng Guo +3
This paper demonstrates how to apply machine learning algorithms to distinguish good stocks from the bad stocks. To this end, we construct 244 technical and fundamental features to…