The promise of least-privilege learning--to find feature representations that are useful for a
learning task but prevent inference of any sensitive information unrelated to this task--is …

We introduce a learning framework called learning using privileged information (LUPI) to the
computer vision field. We focus on the prototypical computer vision problem of teaching …

A wide range of machine learning applications such as privacy-preserving learning,
algorithmic fairness, and domain adaptation/generalization among others, involve learning …

We consider the problem of learning with sensitive features under the privileged information
setting where the goal is to learn a classifier that uses features not available (or too sensitive …

We study the supervised learning paradigm called Learning Using Privileged Information,
first suggested by Vapnik and Vashist (2009). In this paradigm, in addition to the examples …

Many applications of representation learning, such as privacy preservation, algorithmic
fairness, and domain adaptation, desire explicit control over semantic information being …

" Overlearning" means that a model trained for a seemingly simple objective implicitly learns
to recognize attributes and concepts that are (1) not part of the learning objective, and (2) …

Learning using privileged information (LUPI) is a machine learning paradigm which aims at
improving classification by taking advantage of information that is only available at training …

As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …

Information-theoretic measures have been widely adopted in the design of features for
learning and decision problems. Inspired by this, we look at the relationship between i) a …