1 code implementation • NeurIPS 2023 • Mingzhou Liu, Xinwei Sun, Lingjing Hu, Yizhou Wang
Based on these, we can leverage the proxies to remove the bias induced by the hidden variables and hence achieve identifiability.
no code implementations • 17 Jul 2020 • Xinwei Sun, Wenjing Han, Lingjing Hu, Yuan YAO, Yizhou Wang
Specifically, with a variable the splitting term, two estimators are introduced and split apart, i. e. one is for feature selection (the sparse estimator) and the other is for prediction (the dense estimator).
no code implementations • ECCV 2020 • Xinwei Sun, Yilun Xu, Peng Cao, Yuqing Kong, Lingjing Hu, Shanghang Zhang, Yizhou Wang
In this paper, we propose a novel information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation \textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal learning, which is endowed with promising properties: (i) it can utilize effectively the information across different modalities of unlabeled data points to facilitate training classifiers of each modality (ii) it has theoretical guarantee to identify Bayesian classifiers, i. e., the ground truth posteriors of all modalities.
no code implementations • 25 Mar 2015 • Bo Xin, Lingjing Hu, Yizhou Wang, Wen Gao
Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples.