Title:
Reliable Machine Learning from Imperfect Training Data

Abstract:
In many machine learning applications, it is often challenging to collect a
large amount of high-quality labeled data. However, learning from unlabeled
data is not necessarily reliable. To overcome this problem, the use of
imperfect data is promising. In this talk, I will review our recent
research on reliable machine learning from imperfect supervision, including
weakly supervised learning, noisy label learning, and transfer learning.
Finally, I will discuss how machine learning research should evolve in the
era of large foundation models.