Lei Feng

Provably Consistent Partial-Label Learning

2020 · Neural Information Processing Systems

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there …

WREP: A lightweight event real-time processing engine for IoT

2022

The rapid development of the Internet of Things makes it benefit all aspects of human society and greatly facilitate people’s lives. At the same time, the amount of data generated by the Internet of Things …

Partial-Label Regression

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the classification setting where …

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

2023

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply …

Influence-Based Fair Selection for Sample-Discriminative Backdoor Attack

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Backdoor attacks have posed a serious threat in machine learning models, wherein adversaries can poison training samples with maliciously crafted triggers to compromise the victim model. Advanced backdoor attack methods have focused on selectively poisoning …