Many real-world optimization problems contain unknown parameters that mu...
In the context of adversarial robustness, we make three strongly related...
In the last decade, the scientific community has devolved its attention ...
We make two contributions in the field of AI fairness over continuous
pr...
The interplay between Machine Learning (ML) and Constrained Optimization...
It is increasingly common to solve combinatorial optimisation problems t...
The advent of the coronavirus pandemic has sparked the interest in predi...
Numerous real-life decision-making processes involve solving a combinato...
Regularization-based approaches for injecting constraints in Machine Lea...
Methods for taking into account external knowledge in Machine Learning m...
Given enough data, Deep Neural Networks (DNNs) are capable of learning
c...
Machine Learning (ML) models are very effective in many learning tasks, ...
The growing demands of the worldwide IT infrastructure stress the need f...
A variety of computationally challenging constrained optimization proble...
Anomaly detection in supercomputers is a very difficult problem due to t...
In the past few years, the area of Machine Learning (ML) has witnessed
t...
This paper studies the effects of altruism and spitefulness in a two-sid...
In Operation Research, practical evaluation is essential to validate the...