A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

This paper provides a review and commentary on the past, present, and future of numerical
optimization algorithms in the context of machine learning applications. Through case …

We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs)
from data. Our approach is based on a recent algebraic characterization of DAGs that led to …

Graphs are fundamental mathematical structures used in various fields to represent data,
signals, and processes. In this paper, we propose a novel framework for learning/estimating …

Combining simple elements from the literature, we define a linear model that is geared
toward sparse data, in particular implicit feedback data for recommender systems. We show …

Subsequence clustering of multivariate time series is a useful tool for discovering repeated
patterns in temporal data. Once these patterns have been discovered, seemingly …

Value creation is a major sustainability factor for enterprises, in addition to profit
maximization and revenue generation. Modern enterprises collect big data from various …

Many important problems can be modeled as a system of interconnected entities, where
each entity is recording time-dependent observations or measurements. In order to spot …

Federated learning is typically approached as an optimization problem, where the goal is to
minimize a global loss function by distributing computation across client devices that …

Research on big data analytics is entering in the new phase called fast data where multiple
gigabytes of data arrive in the big data systems every second. Modern big data systems …