Recently, learning with soft labels has been shown to achieve better performance than
learning with hard labels in terms of model generalization, calibration, and robustness …

Classification is a fundamental task in science and engineering on which machine learning
methods have shown outstanding performances. However, it is challenging to determine …

In recent years, the merging of vast datasets with powerful computational resources has led
to the emergence of large pre-trained models in the field of deep learning. However, the …

The classic teacher-student model in machine learning posits that a strong teacher
supervises a weak student to improve the student's capabilities. We instead consider the …

Classification is a fundamental task in many applications on which data-driven methods
have shown outstanding performances. However, it is challenging to determine whether …

Learning with inaccurate supervision is often encountered in weakly supervised learning,
and researchers have invested a considerable amount of time and effort in designing …

Fundamental machine learning theory shows that different samples contribute unequally to
both the learning and testing processes. Recent studies on deep neural networks (DNNs) …

The performance of deep neural networks often deteriorates in out-of-distribution settings
due to relying on easy-to-learn but unreliable spurious associations known as shortcuts …