How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this paper, we first describe the generation process of the biased and …

How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this article, we first describe the generation process of the biased and …

Deep metric learning (DML) enables learning with less supervision through its emphasis on
the similarity structure of representations. There has been much work on improving …

Organizations that collect and sell data face increasing scrutiny for the discriminatory use of
data. We propose a novel unsupervised approach to map data into a compressed binary …

Neural networks employ spurious correlations in their predictions, resulting in decreased
performance when these correlations do not hold. Recent works suggest fixing pretrained …

Sequential variational autoencoders (VAEs) with a global latent variable z have been
studied for disentangling the global features of data, which is useful for several downstream …

Investigation of machine learning algorithms robust to changes between the training and test
distributions is an active area of research. In this paper we explore a special type of dataset …

Abstract Machine learning algorithms increasingly support decision-making systems in
contexts where outcomes have long-term implications on the subject's well-being. At issue is …

Abstract Machine Learning (ML) using Artificial Neural Networks (ANNs), referred to as
Deep Learning (DL), is a very popular and powerful method of statistical inference. A …

In many applications of machine learning, it is desirable to have models which not only have
good accuracy on the prediction task but are also “fair” with respect to some protected …