Bootstrap Latent Prototypes for graph positive-unlabeled learning
C Liang, Y Tian, D Zhao, M Li, S Pan, H Zhang, J Wei - Information Fusion, 2024 - Elsevier
Graph positive-unlabeled (GPU) learning aims to learn binary classifiers from only positive
and unlabeled (PU) nodes. The state-of-the-art methods rely on provided class prior …
and unlabeled (PU) nodes. The state-of-the-art methods rely on provided class prior …
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
Detecting anomalies in temporal data has gained significant attention across various real-
world applications, aiming to identify unusual events and mitigate potential hazards. In …
world applications, aiming to identify unusual events and mitigate potential hazards. In …
Learning A Disentangling Representation For PU Learning
In this paper, we address the problem of learning a binary (positive vs. negative) classifier
given Positive and Unlabeled data commonly referred to as PU learning. Although …
given Positive and Unlabeled data commonly referred to as PU learning. Although …
ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
Z Li, M Wei, P Ying, X Xu - arXiv preprint arXiv:2412.02240, 2024 - arxiv.org
Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant
attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift …
attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift …
Fairness-Aware Online Positive-Unlabeled Learning
Abstract Machine learning applications for text classification are increasingly used in
domains such as toxicity and misinformation detection in online settings. However, obtaining …
domains such as toxicity and misinformation detection in online settings. However, obtaining …
Positive and Unlabeled Learning with Controlled Probability Boundary Fence
Positive and Unlabeled (PU) learning refers to a special case of binary classification, and
technically, it aims to induce a binary classifier from a few labeled positive training instances …
technically, it aims to induce a binary classifier from a few labeled positive training instances …