216 citations · 257 across the 12 of their papers we have counts for
11 papers · 1 filter
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Mattia Silvestri, Senne Berden, Jayanta Mandi +4
Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.g., demand or travel time…
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
Mattia Silvestri, Federico Baldo, Eleonora Misino +1
In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis…
UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning
Mattia Silvestri, Allegra De Filippo, Michele Lombardi +1
The interplay between Machine Learning (ML) and Constrained Optimization (CO) has recently been the subject of increasing interest, leading to a new and prolific research area cove…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli +5
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making…
Contrastive Losses and Solution Caching for Predict-and-Optimize
Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti +3
Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class…
An Analysis of Regularized Approaches for Constrained Machine Learning
Michele Lombardi, Federico Baldo, Andrea Borghesi +1
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge. We tackle the issue of findin…
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Andrea Borghesi, Federico Baldo, Michele Lombardi +1
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learni…
Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Fabrizio Detassis, Michele Lombardi, Michela Milano
Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness. Existing approaches typically…
Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak +3
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains,…
Anomaly Detection using Autoencoders in High Performance Computing Systems
Andrea Borghesi, Andrea Bartolini, Michele Lombardi +2
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated an…