The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

High-entropy alloys are solid solutions of multiple principal elements that are capable of
reaching composition and property regimes inaccessible for dilute materials. Discovering …

Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Ockham's razor, the principle of parsimony, states that simpler theories are better than
theories that are more complex. It has a history dating back to Aristotle and it plays an …

We show that artificial neural networks with rectifier units as activation functions can exactly
represent the piecewise affine function that results from the formulation of model predictive …

Tree boosting has empirically proven to be a highly effective approach to predictive
modeling. It has shown remarkable results for a vast array of problems. For many years …

A supervised learning framework is proposed to approximate a model predictive controller
(MPC) with reduced computational complexity and guarantees on stability and constraint …

Adaptive Automation (AA) is a promising approach to keep the task workload demand within
appropriate levels in order to avoid both the under-and over-load conditions, hence …