Foto del docente

Michele Lombardi

Associate Professor

Department of Computer Science and Engineering

Academic discipline: ING-INF/05 Information Processing Systems

Publications

Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns, Contrastive Losses and Solution Caching for Predict-and-Optimize, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, ijcai.org, 2021, pp. 2833 - 2840 (atti di: Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August) [Contribution to conference proceedings]Open Access

Silvestri M.; Lombardi M.; Milano M., Injecting Domain Knowledge in Neural Networks: A Controlled Experiment on a Constrained Problem, in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research, Springer Science and Business Media Deutschland GmbH, 2021, 12735, pp. 266 - 282 (atti di: 18th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2021, Vienna, Austria, 5 - 8 July 2021) [Contribution to conference proceedings]Open Access

De Filippo, Allegra; Lombardi, Michele; Milano, Michela, Integrated Offline and Online Decision Making under Uncertainty, «THE JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH», 2021, 70, pp. 77 - 117 [Scientific article]Open Access

Fioretto F.; Van Hentenryck P.; Mak T.W.K.; Tran C.; Baldo F.; Lombardi M., Lagrangian Duality for Constrained Deep Learning, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, Springer Science and Business Media Deutschland GmbH, 2021, 12461, pp. 118 - 135 (atti di: European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Ghent, Belgium, 2020) [Contribution to conference proceedings]

Fabrizio Detassis, Michele Lombardi, Michela Milano, Teaching the Old Dog New Tricks: Supervised Learning with Constraints, in: Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}2021, Thirty-Third Conference on Innovative Applications of ArtificialIntelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advancesin Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,2021, {AAAI} Press, 2021, 35, pp. 3742 - 3749 (atti di: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual event, 2-9 February) [Contribution to conference proceedings]

Borghesi, Andrea; Tagliavini, Giuseppe; Lombardi, Michele; Benini, Luca; Milano, Michela, Combining learning and optimization for transprecision computing, in: Proceedings of the 17th ACM International Conference on Computing Frontiers, 2020, pp. 10 - 18 (atti di: 17th ACM International Conference on Computing Frontiers, Catania, Italy, 1-10 June 2020) [Contribution to conference proceedings]Open Access

De Filippo A.; Lombardi M.; Milano M., Hybrid offline/online optimization under uncertainty, in: Frontiers in Artificial Intelligence and Applications, IOS Press BV, 2020, 325, pp. 2899 - 2900 (atti di: 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020, esp, 2020) [Contribution to conference proceedings]Open Access

Silvestri M.; Lombardi M.; Milano M., Injecting domain knowledge in neural networks: A controlled experiment on a constrained problem, in: CEUR Workshop Proceedings, CEUR-WS, 2020, 2659, pp. 52 - 58 (atti di: 1st International Workshop on New Foundations for Human-Centered AI, NeHuAI 2020, Santiago de Compostella, Spain, September 4, 2020) [Contribution to conference proceedings]Open Access

Andrea Borghesi, Federico Baldo, Michele Lombardi, Michela Milano, Injective Domain Knowledge in Neural Networks for Transprecision Computing, in: Machine Learning, Optimization, and Data Science. LOD 2020, Springer, 2020, 12565, pp. 587 - 600 (atti di: The Sixth International Conference on Machine Learning, Optimization, and Data Science, Siena, July 19-23, 2020) [Contribution to conference proceedings]Open Access

Detassis F.; Lombardi M.; Milano M., Teaching the old dog new tricks: Supervised learning with constraints, in: CEUR Workshop Proceedings, Aachen, CEUR-WS, 2020, 2659, pp. 44 - 51 (atti di: 1st International Workshop on New Foundations for Human-Centered AI, NeHuAI 2020, esp, 2020) [Contribution to conference proceedings]Open Access

De Filippo A.; Lombardi M.; Milano M., The blind men and the elephant: Integrated offline/online optimization under uncertainty, in: PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, International Joint Conferences on Artificial Intelligence, «IJCAI», 2020, 2021-, pp. 4840 - 4846 (atti di: 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, jpn, 2021) [Contribution to conference proceedings]Open Access

Chisca D.S.; Lombardi M.; Milano M.; O'Sullivan B., A Sampling-Free Anticipatory Algorithm for the Kidney Exchange Problem, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, 11494, pp. 146 - 162 (atti di: 16th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2019, grc, 2019) [Contribution to conference proceedings]

Borghesi A.; Bartolini A.; Lombardi M.; Milano M.; Benini L., A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems, «ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE», 2019, 85, pp. 634 - 644 [Scientific article]Open Access

Borghesi, Andrea; Bartolini, Andrea; Lombardi, Michele; Milano, Michela; Benini, Luca, Anomaly Detection Using Autoencoders in High Performance Computing Systems, in: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019, 33, pp. 9428 - 9433 (atti di: Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, US, 28 Jan - 02 Feb 2019) [Contribution to conference proceedings]

De Filippo, Allegra; Lombardi, Michele; Milano, Michela, How to Tame Your Anticipatory Algorithm, in: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019, pp. 1071 - 1077 (atti di: International Joint Conference on Artificial Intelligence, Macao, Agosto 2019) [Contribution to conference proceedings]

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