Haobo Wang

The Emerging Trends of Multi-Label Learning

2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is …

Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility

2023 · arXiv (Cornell University)

The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-sourced models, and plugins. However, with their widespread deployment, there …

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 …