11 citations · 27 across the 8 of their papers we have counts for
8 papers
MISNN: Multiple Imputation via Semi-parametric Neural Networks
Zhiqi Bu, Zongyu Dai, Yiliang Zhang +1
Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream da…
Label-Noise Learning with Intrinsically Long-Tailed Data
Yang Lu, Yiliang Zhang, Bo Han +2
Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth class…
Assessing Fairness in the Presence of Missing Data
Yiliang Zhang, Qi Long
Missing data are prevalent and present daunting challenges in real data analysis. While there is a growing body of literature on fairness in analysis of fully observed data, there…
Fairness in Missing Data Imputation
Yiliang Zhang, Qi Long
Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. T…
An Unconstrained Layer-Peeled Perspective on Neural Collapse
Wenlong Ji, Yiping Lu, Yiliang Zhang +2
Neural collapse is a highly symmetric geometric pattern of neural networks that emerges during the terminal phase of training, with profound implications on the generalization perf…
Efficient Designs of SLOPE Penalty Sequences in Finite Dimension
Yiliang Zhang, Zhiqi Bu
In linear regression, SLOPE is a new convex analysis method that generalizes the Lasso via the sorted L1 penalty: larger fitted coefficients are penalized more heavily. This magnit…
A general kernel boosting framework integrating pathways for predictive modeling based on genomic data
Li Zeng, Zhaolong Yu, Yiliang Zhang +1
Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailo…
Inference of Dynamic Graph Changes for Functional Connectome
Dingjue Ji, Junwei Lu, Yiliang Zhang +2
Dynamic functional connectivity is an effective measure for the brain's responses to continuous stimuli. We propose an inferential method to detect the dynamic changes of brain net…