no code implementations • 25 Oct 2022 • Mattia Silvestri, Allegra De Filippo, Michele Lombardi, Michela Milano
Our approach relies on a clever decomposition of the policy in two stages, namely an unconstrained ML model and a CO problem, to take advantage of the strength of each approach while compensating for its weaknesses.
no code implementations • 3 Mar 2021 • Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli, Riccardo Scheda, Michele Lombardi, Andrea Borghesi, Stefano Diciotti, Michela Milano
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 processes.
no code implementations • 20 May 2020 • Michele Lombardi, Federico Baldo, Andrea Borghesi, Michela Milano
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge.
no code implementations • 19 May 2020 • Andrea Borghesi, Federico Baldo, Michela Milano
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets.
no code implementations • 25 Feb 2020 • 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.
no code implementations • 25 Feb 2020 • Mattia Silvestri, Michele Lombardi, Michela Milano
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy.
2 code implementations • 24 Feb 2020 • Andrea Borghesi, Giuseppe Tagliavini, Michele Lombardi, Luca Benini, Michela Milano
The ML model learns the relation between variables precision and the output error; this information is then embedded in the MP focused on minimizing the number of bits.
Distributed, Parallel, and Cluster Computing
1 code implementation • 24 Feb 2020 • Andrea Borghesi, Federico Baldo, Michele Lombardi, Michela Milano
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets.
5 code implementations • 13 Nov 2018 • Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, Luca Benini
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
no code implementations • 15 Jul 2018 • Michele Lombardi, Michela Milano
The three pillars of constraint satisfaction and optimization problem solving, i. e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness.
no code implementations • 15 May 2014 • Marco Gavanelli, Stefano Bragaglia, Michela Milano, Federico Chesani, Elisa Marengo, Paolo Cagnoli
In the policy making process a number of disparate and diverse issues such as economic development, environmental aspects, as well as the social acceptance of the policy, need to be considered.