Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Supervised learning under label noise has seen numerous advances recently, while
existing theoretical findings and empirical results broadly build up on the class-conditional …

In learning with noisy labels, for every instance, its label can randomly walk to other classes
following a transition distribution which is named a noise model. Well-studied noise models …

This paper presents the first review of noise models in classification covering both label and
attribute noise. Their study reveals the lack of a unified nomenclature in this field. In order to …

Risk assessment tools are widely used around the country to inform decision making within
the criminal justice system. Recently, considerable attention has been devoted to the …

Abstract Deep Neural Networks (DNNs) generally require large-scale datasets for training.
Since manually obtaining clean labels for large datasets is extremely expensive …

Classification datasets created from chemical processes can be affected by errors, which
impair the accuracy of the models built. This fact highlights the importance of analyzing the …

Artificial intelligence (AI) is a relatively new scientific area that emerged from the efforts of a
handful of scientists from diverse fields approximately 70 years ago. The achievements of AI …

Today's classification task is getting more and more complex. This inevitably renders
unanticipated compromises on the quality of data labels. In this paper, we consider learning …

Robust learning methods aim to learn a clean target distribution from noisy and corrupted
training data where a specific corruption pattern is often assumed a priori. Our proposed …