Zhengzhang Hou — accepted AI-conference papers

2026

Denoising Mixup for Regression

Zhengzhang Hou, Zhanshan Li, Yanbo Liu, Geoff Nitschke, You Lu, Ximing Li

AAAI 2026technical

Data augmentation is an intuitive solution to increase the diversity of training instances in the machine learning community. Mixup is acknowledged as an effective and efficient mix-based data augmentation method, following a linear alignment assumption that the linear interpolations of features ali

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2025

A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study

Yuanchao Dai, Zhengzhang Hou, Changchun Li, Yuanbo Xu, En Wang, Ximing Li

NeurIPS 2025poster

Positive-Unlabeled (PU) learning refers to a specific weakly-supervised learning paradigm that induces a binary classifier with a few positive labeled instances and massive unlabeled instances. To handle this task, the community has proposed dozens of PU learning methods with various techniques, dem…

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