Zobrazeno 1 - 10
of 2 586
pro vyhledávání: '"Du, Jin"'
Data integration has become increasingly common in aligning multiple heterogeneous datasets. With high-dimensional outcomes, data integration methods aim to extract low-dimensional embeddings of observations to remove unwanted variations, such as bat
Externí odkaz:
http://arxiv.org/abs/2410.04996
Common practice in modern machine learning involves fitting a large number of parameters relative to the number of observations. These overparameterized models can exhibit surprising generalization behavior, e.g., ``double descent'' in the prediction
Externí odkaz:
http://arxiv.org/abs/2410.01259
We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent estimator for the risk. Specifically, we consider a heterogeneous coll
Externí odkaz:
http://arxiv.org/abs/2409.15252
Autor:
Du, Jin, Zhang, Xinhe, Shen, Hao, Xian, Xun, Wang, Ganghua, Zhang, Jiawei, Yang, Yuhong, Li, Na, Liu, Jia, Ding, Jie
Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrophic forgetting. Recent neuroscience research suggests that neural acti
Externí odkaz:
http://arxiv.org/abs/2409.13997
Autor:
Du, Jin-Hong, Patil, Pratik
We study the implicit regularization effects induced by (observation) weighting of pretrained features. For weight and feature matrices of bounded operator norms that are infinitesimally free with respect to (normalized) trace functionals, we derive
Externí odkaz:
http://arxiv.org/abs/2408.15784
Autor:
Zhou, Wenbin, Du, Jin-Hong
Spatial transcriptomics data is invaluable for understanding the spatial organization of gene expression in tissues. There have been consistent efforts in studying how to effectively utilize the associated spatial information for refining gene expres
Externí odkaz:
http://arxiv.org/abs/2408.00911
Autor:
Zhen, Yaoming, Du, Jin-Hong
Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses
Externí odkaz:
http://arxiv.org/abs/2407.04104
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-cell-level measurements. However, the individual gene expression levels
Externí odkaz:
http://arxiv.org/abs/2404.09119
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 distribution. We establish general conditions that determine the sign of
Externí odkaz:
http://arxiv.org/abs/2404.01233