15 citations · 49 across the 18 of their papers we have counts for
8 papers · 1 filter
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…