Generalized robust loss functions for machine learning.

Loss function is a critical component of machine learning. Some robust loss functions are proposed to mitigate the adverse effects caused by noise. However, they still face many challenges.

Linear-exponential loss incorporated deep learning for imbalanced classification.

The class imbalance issue is a pretty common and enduring topic all the time. When encountering unbalanced data distribution, conventional methods are prone to classify minority samples as majority ones, which may cause severe consequences in reality. It is crucial yet challenging to cope with such problems.

Partial label learning: Taxonomy, analysis and outlook.

Partial label learning (PLL) is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the set is the ground-truth label. In this paper, we propose a novel taxonomy framework for PLL including four categories: disambiguation strategy, transformation strategy, theory-oriented strategy and extensions.