Trustworthy Machine Learning from Data to Models

Trustworthy machine learning seeks to handle critical problems in addressing the issues of robustness, privacy, security, reliability, and other desirable properties. The broad research area has achieved remarkable advancement and brings various emerging topics along with the progress. This course provides a systematic overview of the research problems under trustworthy machine learning covering the perspectives from data to model. Starting with fundamental data-centric learning, the course reviews learning with noisy data, long-tailed distribution, out-of-distribution data, and adversarial examples to achieve robustness. Delving into private and secured learning, this course elaborates on core methodologies of differential privacy, different attacking threats, and learning paradigms, to realize privacy protection and enhance security. Meanwhile, this course introduces several trendy issues related to the foundation models, including jailbreak prompts, watermarking, and hallucination, as well as causal learning and reasoning. To sum up, this course integrates commonly isolated research problems in a unified manner, which provides general problem setups, detailed sub-directions, and further discussion on its challenges or future developments.

• Lecture I: Trustworthy Data-centric Learning
• Lecture II: Trustworthy Private and Secured Learning
• Lecture III: Trustworthy Foundation Models
• Lecture IV: Advanced Topics in Trustworthy Machine Learning

• ESSAI 2026 course listing
• TMLR Group