In contrast to ordinary supervised classification tasks that require massive data with high-
quality labels, complementary-label learning (CLL) deals with the weakly-supervised …

Label Ranking (LR) is the supervised task of learning a sorting function that maps feature
vectors $ x\in\mathbb {R}^ d $ to rankings $\sigma (x)\in\mathbb S_k $ over a finite set of $ k …

Label ranking tasks are concerned with the problem of ranking a finite set of labels for each
instance according to their relevance. Boosting is a well-known and reliable ensemble …

In multi-label learning, the high dimensions of both label and feature spaces pose great
challenges to multi-label classification. In this paper, we propose dual projection learning …

The datasets used in machine learning and statistics are huge and often imperfect, eg, they
contain corrupted data, examples with wrong labels, or hidden biases. Most existing …

In this thesis we theoretically study questions in the area of Reliable Machine Learning in
order to design algorithms that are robust to bias and noise (Robust Machine Learning) and …