research
∙
07/11/2023
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Many real-world optimization problems contain unknown parameters that mu...
share
research
∙
06/25/2023
Computational Asymmetries in Robust Classification
In the context of adversarial robustness, we make three strongly related...
research
∙
06/17/2023
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
In the last decade, the scientific community has devolved its attention ...
research
∙
05/29/2023
Generalized Disparate Impact for Configurable Fairness Solutions in ML
We make two contributions in the field of AI fairness over continuous pr...
research
∙
10/25/2022
UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning
The interplay between Machine Learning (ML) and Constrained Optimization...
research
∙
05/20/2022
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
It is increasingly common to solve combinatorial optimisation problems t...
research
∙
03/03/2021
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
The advent of the coronavirus pandemic has sparked the interest in predi...
research
∙
11/10/2020
Discrete solution pools and noise-contrastive estimation for predict-and-optimize
Numerous real-life decision-making processes involve solving a combinato...
research
∙
05/20/2020
An Analysis of Regularized Approaches for Constrained Machine Learning
Regularization-based approaches for injecting constraints in Machine Lea...
research
∙
02/25/2020
Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Methods for taking into account external knowledge in Machine Learning m...
research
∙
02/25/2020
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Given enough data, Deep Neural Networks (DNNs) are capable of learning c...
research
∙
02/24/2020
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Machine Learning (ML) models are very effective in many learning tasks, ...
research
∙
02/24/2020
Combining Learning and Optimization for Transprecision Computing
The growing demands of the worldwide IT infrastructure stress the need f...
research
∙
01/26/2020
A Lagrangian Dual Framework for Deep Neural Networks with Constraints
A variety of computationally challenging constrained optimization proble...
research
∙
11/13/2018
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to t...
research
∙
07/15/2018
Boosting Combinatorial Problem Modeling with Machine Learning
In the past few years, the area of Machine Learning (ML) has witnessed t...
research
∙
03/11/2018
On Trade in Bilateral Oligopolies with Altruistic and Spiteful Agents
This paper studies the effects of altruism and spitefulness in a two-sid...
research
∙
03/16/2017
