{"type":"ScholarlyArticle","title":"Integrating Machine Learning and Optimization to Boost Decision Making","authors":[{"name":"Ferdinando Fioretto","slug":"ferdinando-fioretto","given":"Ferdinando","family":"Fioretto","affiliation":"Syracuse University"}],"abstract":"This paper presents a conceptual review of our recent advancements in the integration of machine learning and optimization. It focuses on describing new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference.","doi":"10.24963/ijcai.2022/815","url":"https://ijcai.org/proceedings/2022/815","pdf":"https://ijcai.org/proceedings/2022/0815.pdf","markdown":"https://ijcai.org/proceedings/2022/815.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/815","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Early Career","track":"Early Career","number":815,"pages":{"first":5808,"last":5812},"keywords":["EC: Machine Learning","EC: Combinatorial Optimization"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987224","length_sec":58,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985367","length_sec":900,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bengio et al., 2020] Yoshua Bengio, Andrea Lodi, and An- toine Prouvost. Machine learning for combinatorial op- timization: a methodological tour d'horizon. European Journal of Operational Research, 2020.","title":"Machine learning for combinatorial optimization: a methodological tour d'horizon","authors":"Yoshua Bengio, Andrea Lodi, Antoine Prouvost","year":"2020","venue":"European Journal of Operational Research","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chatzos et al., 2020] Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak, and Pascal Van Hentenryck. High- fidelity machine learning approximations of large-scale optimal power flow. CoRR, abs/2006.16356, 2020.","title":"Highfidelity machine learning approximations of large-scale optimal power flow","authors":"Chatzos","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dinh et al., 2021] My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, and Kyri Baker. Towards understanding the unreasonable effectiveness of learning AC-OPF solutions. CoRR, abs/2111.11168, 2021.","title":"Towards understanding the unreasonable effectiveness of learning AC-OPF solutions","authors":"My H Dinh, Ferdinando Fioretto, Mostafa Mohammadian, Kyri Baker","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Fioretto et al., 2020a] Ferdinando Fioretto, Pascal Van Hen- tenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, and Michele Lombardi. Lagrangian duality for con- strained deep learning. In European Conference on Ma- chine Learning and Knowledge Discovery in Databases (ECML-PKDD), volume 12461, pages 118-135, 2020.","title":"Lagrangian duality for constrained deep learning","authors":"Fioretto","year":"2020","venue":"European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Fioretto et al., 2020b] Ferdinando Fioretto, Terrence W.K. Mak, and Pascal Van Hentenryck. Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 630-637, 2020.","title":"Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods","authors":"Fioretto","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Kaur et al., 2022] Sawinder Kaur, Ferdinando Fioretto, and Asif Salekin. Deadwooding: Robust global pruning for deep neural networks. CoRR, abs/2202.05226, 2022.","title":"Deadwooding: Robust global pruning for deep neural networks","authors":"Kaur","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kotary et al., 2021a] James Kotary, Ferdinando Fioretto, and Pascal Van Hentenryck. Learning hard optimization problems: A data generation perspective. Advances in Neural Information Processing Systems, 34, 2021.","title":"Learning hard optimization problems: A data generation perspective","authors":"Kotary","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kotary et al., 2021b] James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Bryan Wilder. End-to-end constrained optimization learning: A survey. In Proceed- ings of the International Joint Conference on Artificial In- telligence (IJCAI), pages 4475-4482, 2021.","title":"End-to-end constrained optimization learning: A survey","authors":"Kotary","year":"2021","venue":"Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2021/610"},{"position":9,"raw":"[Kotary et al., 2022a] James Kotary, Ferdinando Fioretto, and Pascal Van Hentenryck. Fast approximations for job shop scheduling: A lagrangian dual deep learning method. In Proceedings of the AAAI Conference on Artificial Intel- ligence (AAAI), page to appear, 2022.","title":"Fast approximations for job shop scheduling: A lagrangian dual deep learning method","authors":"Kotary","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kotary et al., 2022b] James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Ziwei Zhu. End-to-end learn- ing for fair ranking systems. In Proceedings of the ACM Web Conference (WWW), pages 3520-3530, 2022.","title":"End-to-end learning for fair ranking systems","authors":"Kotary","year":"2022","venue":"Proceedings of the ACM Web Conference (WWW)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Mak et al., 2021] Terrence W. K. Mak, Ferdinando Fioretto, and Pascal Van Hentenryck. Load embeddings for scalable AC-OPF learning. CoRR, abs/2101.03973, 2021.","title":"Load embeddings for scalable AC-OPF learning","authors":"Terrence W K Mak, Ferdinando Fioretto, Pascal Van Hentenryck","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Moon, 2021] Sunghwan Moon. Relu network with bounded width is a universal approximator in view of an approxi- mate identity. Applied Sciences, 11(1):427, 2021.","title":"Sunghwan Moon. Relu network with bounded width is a universal approximator in view of an approximate identity","authors":"Moon","year":"2021","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Nagar et al., 2021] Anudit Nagar, Cuong Tran, and Ferdi- nando Fioretto. Privacy-preserving and accountable multi- agent learning. In Proceedings of the International Confer- ence on Autonomous Agents and Multiagent Systems (AA- MAS), pages 1605-1606, 2021.","title":"Privacy-preserving and accountable multiagent learning","authors":"Nagar","year":"2021","venue":"Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AA-MAS)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Tran et al., 2021a] Cuong Tran, My H. Dinh, and Ferdi- nando Fioretto. Differentially private empirical risk min- imization under the fairness lens. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), pages 27555-27565, 2021.","title":"Differentially private empirical risk minimization under the fairness lens","authors":"Tran","year":"2021","venue":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tran et al., 2021b] Cuong Tran, Ferdinando Fioretto, and Pascal Van Hentenryck. Differentially private and fair deep learning: A lagrangian dual approach. In Proceed- ings of the AAAI Conference on Artificial Intelligence (AAAI), pages 9932-9939, 2021.","title":"Differentially private and fair deep learning: A lagrangian dual approach","authors":"Tran","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Tran et al., 2022] Cuong Tran, Keyu Zhu, Ferdinando Fioretto, and Pascal Van Hentenryck. SF-PATE: scalable, fair, and private aggregation of teacher ensembles. CoRR, abs/2204.05157, 2022.","title":"SF-PATE: scalable, fair, and private aggregation of teacher ensembles","authors":"Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck","year":"2022","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2023/56"},{"position":17,"raw":"[Vesselinova et al., 2020] Natalia Vesselinova, Rebecca Steinert, Daniel F Perez-Ramirez, and Magnus Boman. Learning combinatorial optimization on graphs: A survey with applications to networking. IEEE Access, 8:120388-120416, 2020.","title":"Learning combinatorial optimization on graphs: A survey with applications to networking","authors":"Natalia Vesselinova, Rebecca Steinert, Magnus Daniel F Perez-Ramirez, Boman","year":"2020","venue":"IEEE Access","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}