1 code implementation • 22 Sep 2023 • Lorenzo Cellini, Antonio Macaluso, Michele Lombardi

The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions.

no code implementations • 11 Jul 2023 • Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali İrfan Mahmutoğulları, Brandon Amos, Tias Guns, Michele Lombardi

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 times in delivery problems).

1 code implementation • 25 Jun 2023 • Samuele Marro, Michele Lombardi

In the context of adversarial robustness, we make three strongly related contributions.

1 code implementation • 17 Jun 2023 • Mattia Silvestri, Federico Baldo, Eleonora Misino, Michele Lombardi

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 of a plethora of phenomena.

1 code implementation • 29 May 2023 • Luca Giuliani, Eleonora Misino, Michele Lombardi

We make two contributions in the field of AI fairness over continuous protected attributes.

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 • 20 May 2022 • Stefano Teso, Laurens Bliek, Andrea Borghesi, Michele Lombardi, Neil Yorke-Smith, Tias Guns, Andrea Passerini

The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.

no code implementations • 30 May 2021 • Ville Korpela, Michele Lombardi, Riccardo D. Saulle

We study rotation programs within the standard implementation frame-work under complete information.

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.

2 code implementations • 10 Nov 2020 • Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns

Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data.

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 • 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.

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.

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.

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

no code implementations • 26 Jan 2020 • Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak, Cuong Tran, Federico Baldo, Michele Lombardi

In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.

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 • 16 Mar 2017 • Sascha Van Cauwelaert, Michele Lombardi, Pierre Schaus

In Operation Research, practical evaluation is essential to validate the efficacy of optimization approaches.

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