10 citations · 29 across the 12 of their papers we have counts for
6 papers · 1 filter
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
Zhonghao Wang, Danyu Sun, Sheng Zhou +4
Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node…
Energy-based Automated Model Evaluation
Ru Peng, Heming Zou, Haobo Wang +3
The conventional evaluation protocols on machine learning models rely heavily on a labeled, i.i.d-assumed testing dataset, which is not often present in real world applications. Th…
Regression with Cost-based Rejection
Xin Cheng, Yuzhou Cao, Haobo Wang +3
Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous…
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
Renyu Zhu, Haoyu Liu, Runze Wu +4
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambigui…
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
Senlin Shu, Shuo He, Haobo Wang +3
In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes…
Deep Partial Multi-Label Learning with Graph Disambiguation
Haobo Wang, Shisong Yang, Gengyu Lyu +5
In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Rec…