Example-based explanations are widely used in the effort to improve the interpretability of
highly complex distributions. However, prototypes alone are rarely sufficient to represent the …

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in
subsequent image processing and analysis. Visual inspection is subjective and impractical …

Performance metrics for binary classification are designed to capture tradeoffs between four
fundamental population quantities: true positives, false positives, true negatives and false …

Multilabel classification is rapidly developing as an important aspect of modern predictive
modeling, motivating study of its theoretical aspects. To this end, we propose a framework for …

Metric Elicitation is a principled framework for selecting the performance metric that best
reflects implicit user preferences. However, available strategies have so far been limited to …

In many problem settings, parameter vectors are not merely sparse, but dependent in such
a way that non-zero coefficients tend to cluster together. We refer to this form of dependency …

We present a general framework for constructing prior distributions with structured variables.
The prior is defined as the information projection of a base distribution onto distributions …

Despite the immense success that deep neural networks (DNNs) have achieved, \emph{adversarial
examples}, which are perturbed inputs that aim to mislead DNNs to make mistakes, …

This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the
Correspondence-LDA model that allows for variable spatial representations to be associated …

We propose a novel and efficient algorithm for the collaborative preference completion
problem, which involves jointly estimating individualized rankings for a set of entities over a …