4 citations · 9 across the 5 of their papers we have counts for
13 papers
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…
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Mattia Silvestri, Michele Lombardi, Michela Milano
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy. In several domains, however, data is scarce or expensive t…