2 citations · 3 across the 5 of their papers we have counts for
5 papers
Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations
Yangbangyan Jiang, Qianqian Xu, Huiyang Shao +4
As a variant of the Area Under the ROC Curve (AUC), the partial AUC (PAUC) focuses on a specific range of false positive rate (FPR) and/or true positive rate (TPR) in the ROC curve…
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
Huiyang Shao, Xin Xia, Yuhong Yang +3
Diffusion models have achieved remarkable success across various domains. However, their slow generation speed remains a critical challenge. Existing acceleration methods, while ai…
Towards Demystifying the Generalization Behaviors When Neural Collapse Emerges
Peifeng Gao, Qianqian Xu, Yibo Yang +5
Neural Collapse (NC) is a well-known phenomenon of deep neural networks in the terminal phase of training (TPT). It is characterized by the collapse of features and classifier into…
A Study of Neural Collapse Phenomenon: Grassmannian Frame, Symmetry and Generalization
Peifeng Gao, Qianqian Xu, Peisong Wen +3
In this paper, we extend original Neural Collapse Phenomenon by proving Generalized Neural Collapse hypothesis. We obtain Grassmannian Frame structure from the optimization and gen…
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
Huiyang Shao, Qianqian Xu, Zhiyong Yang +2
The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifie…