5 papers
Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees
Miao Zhang, Junpeng Li, ChangChun HUa +1
Weakly supervised learning often operates with coarse aggregate signals rather than instance labels. We study a setting where each training example is an -tuple containing exact…
Cost-Sensitive Unbiased Risk Estimation for Multi-Class Positive-Unlabeled Learning
Miao Zhang, Junpeng Li, Changchun Hua +1
Positive--Unlabeled (PU) learning considers settings in which only positive and unlabeled data are available, while negatives are missing or left unlabeled. This situation is commo…
Structure-Preserving Margin Distribution Learning for High-Order Tensor Data with Low-Rank Decomposition
Yang Xu, Junpeng Li, Changchun Hua +1
The Large Margin Distribution Machine (LMDM) is a recent advancement in classifier design that optimizes not just the minimum margin (as in SVM) but the entire margin distribution,…
Learning from M-Tuple Dominant Positive and Unlabeled Data
Jiahe Qin, Junpeng Li, Changchun Hua +1
Label Proportion Learning (LLP) addresses the classification problem where multiple instances are grouped into bags and each bag contains information about the proportion of each c…
A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
Shuying Huang, Junpeng Li, Changchun Hua +1
To alleviate the annotation burden in supervised learning, N-tuples learning has recently emerged as a powerful weakly-supervised method. While existing N-tuples learning approache…