The notion of task similarity is at the core of various machine learning paradigms, such as
domain adaptation and meta-learning. Current methods to quantify it are often heuristic …

Patient-derived organoids (PDOs) can model personalized therapy responses; however,
current screening technologies cannot reveal drug response mechanisms or how tumor …

Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

Optimal transport (OT) has become exceedingly popular in machine learning, data science,
and computer vision. The core assumption in the OT problem is the equal total amount of …

We consider statistical methods that invoke a min-max distributionally robust formulation to
extract good out-of-sample performance in data-driven optimization and learning problems …

Instead of searching the entire network directly, current NAS approaches increasingly
search for multiple relatively small cells to reduce search costs. A major challenge is to …

Abstract The Optimal Transport (aka Wasserstein) distance is an increasingly popular
similarity measure for rich data domains, such as images or text documents. This raises the …

Optimal transport distances have become a classic tool to compare probability distributions
and have found many applications in machine learning. Yet, despite recent algorithmic …

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …

The word mover's distance (WMD) is a fundamental technique for measuring the similarity of
two documents. As the crux of WMD, it can take advantage of the underlying geometry of the …