Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …

This chapter addresses the role that data‐driven approaches, especially machine learning
methods, are expected to play in materials research in the immediate future. Machine …

We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

Computing optimal transport distances such as the earth mover's distance is a fundamental
problem in machine learning, statistics, and computer vision. Despite the recent introduction …

Deep models that are both effective and explainable are desirable in many settings; prior
explainable models have been unimodal, offering either image-based visualization of …

Autoregressive sampling from large language models has led to state-of-the-art results in
several natural language tasks. However, autoregressive sampling generates tokens one at …

Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …

Abstract We present the Word Mover's Distance (WMD), a novel distance function between
text documents. Our work is based on recent results in word embeddings that learn …

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics
for measuring the similarity between two point sets. However, CD is usually insensitive to …