The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Modern software applications are increasingly configurable, which puts a burden on users to
tune these configurations for their target hardware and workloads. To help users, machine …

We propose the use of machine learning techniques in the Bayesian framework for the
prediction of tidal currents. Computer algorithms based on the classical harmonic analysis …

Abstract Multiresolution Matrix Factorization (MMF) is unusual amongst fast matrix
factorization algorithms in that it does not make a low rank assumption. This makes MMF …

In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders
(MGVAE), the first hierarchical generative model to learn and generate graphs in a …

The current best practice for computing optimal transport (OT) is via entropy regularization
and Sinkhorn iterations. This algorithm runs in quadratic time as it requires the full pairwise …

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports,
and climate science. Tensor latent factor models can describe higher-order correlations for …

Objective: To determine the completeness of argumentative steps necessary to conclude
effectiveness of an algorithm in a sample of current ML/AI supervised learning literature …

Kernel methods are a popular technique for extending linear models to handle non-linear
spatial problems via a mapping to an implicit, high-dimensional feature space. While kernel …

Bayesian optimization (BO) is a powerful class of data-driven techniques for the
maximization of expensive-to-evaluate objective functions. These techniques construct a …