In this paper, we consider the problem of finding high dimensional power means: given a set
$ A $ of $ n $ points in $\R^ d $, find the point $ m $ that minimizes the sum of Euclidean …

The acquisition of labels for supervised learning can be expensive. To improve the sample
efficiency of neural network regression, we study active learning methods that adaptively …

The vast advances in Machine Learning over the last ten years have been powered by the
availability of suitably prepared data for training purposes. The future of ML-enabled …

The application of clustering algorithms is expanding due to the rapid growth of data
volumes. Nevertheless, existing algorithms are not always effective because of high …

In many cases, a service provider might require to aggregate data from end-users to perform
mining tasks such as K-means clustering. Nevertheless, since such data often contain …

Least-mean squares (LMS) solvers such as Linear/Ridge/Lasso-Regression, SVD and
Elastic-Net not only solve fundamental machine learning problems, but are also the building …

Appropriate training data is a requirement for building good machine-learned models. In this
paper, we study the notion of coverage for ordinal and continuous-valued attributes, by …

With the growing volumes of vehicle trajectory data, it becomes increasingly possible to
capture time-varying and uncertain travel costs, eg, travel time, in a road network. The …

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling
continuous spatial data. However, they often rely on Euclidean distances to construct the …

We study kernel quadrature rules with convex weights. Our approach combines the spectral
properties of the kernel with recombination results about point measures. This results in …