1 code implementation • 20 Dec 2023 • Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang
The model trained on noisy labels serves as a `bad teacher' in knowledge distillation, aiming to decrease the risk of providing incorrect information.
no code implementations • 2 May 2023 • Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long
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 data analysis.
1 code implementation • ICCV 2023 • Yang Lu, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang
In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution.
no code implementations • NeurIPS 2021 • Yiliang Zhang, Qi Long
When the goal is to develop a fair algorithm in the complete data domain where there are no missing values, an algorithm that is fair in the complete case domain may show disproportionate bias towards some marginalized groups in the complete data domain.
no code implementations • 22 Oct 2021 • 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.
no code implementations • ICLR 2022 • Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J. Su
We prove that gradient flow on this model converges to critical points of a minimum-norm separation problem exhibiting neural collapse in its global minimizer.
no code implementations • NeurIPS 2021 • Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J Su
In this paper, we derive a landscape analysis to the surrogate model to study the inductive bias of the neural features and parameters from neural networks with cross-entropy.
1 code implementation • 14 Feb 2021 • Yiliang Zhang, Zhiqi Bu
In this paper, we propose two efficient algorithms to design the possibly high-dimensional SLOPE penalty, in order to minimize the mean squared error.
no code implementations • 1 Jan 2021 • Yiliang Zhang, Qi Long
While there is a growing body of literature on fairness in analysis of fully observed data, there has been little work on investigating fairness in analysis of incomplete data when the goal is to develop a fair algorithm in the complete data domain where there are no missing values.
1 code implementation • 26 Aug 2020 • Li Zeng, Zhaolong Yu, Yiliang Zhang, Hongyu Zhao
Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently.