automated machine learning (AutoML) aims to select and configure machine
learning algorithms and combine them into machine learning pipelines tailored
to a dataset at hand. For supervised learning tasks, most not
Scalable Label Distribution Learning (SLDL) is proposed for multi-label classification, where different labels are described as distributions in a latent space with asymmetric correlation, independent of the number of labels, resulting in little computational consumption.