Optimization of image transformation functions for the purpose of data augmentation has
been intensively studied. In particular, adversarial data augmentation strategies, which …

Sorting is used pervasively in machine learning, either to define elementary algorithms, such
as $ k $-nearest neighbors ($ k $-NN) rules, or to define test-time metrics, such as top-$ k …

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source
domain to understand a similar yet distinct unlabeled target domain. Notably, global …

A growing number of generative statistical models do not permit the numerical evaluation of
their likelihood functions. Approximate Bayesian computation has become a popular …

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …

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 …

Making sense of Wasserstein distances between discrete measures in high-dimensional
settings remains a challenge. Recent work has advocated a two-step approach to improve …

In this paper we introduce a Wasserstein-type distance on the set of Gaussian mixture
models. This distance is defined by restricting the set of possible coupling measures in the …

Gaussian mixture models (GMM) are powerful parametric tools with many applications in
machine learning and computer vision. Expectation maximization (EM) is the most popular …

Learning an effective representation of 3D point clouds requires a good metric to measure
the discrepancy between two 3D point sets, which is non-trivial due to their irregularity. Most …