Extending the Moving Targets Method for Injecting Constraints in Machine Learning

Giuliani, Luca (2021) Extending the Moving Targets Method for Injecting Constraints in Machine Learning. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270]
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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Giuliani, Luca
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
machine learning,deep learning,artificial intelligence,AI,ML,DL,symbolic AI,sub-symbolic AI,informed machine learning,constrained machine learning,optimization,combinatorial optimization,MILP,linear programming,mixed-integer programming,neuro-symbolic AI,explainable AI,shape constraints,regularization,semi-supervised learning,tensorflow,keras,scikit-learn,python,pandas,numpy,lattice models
Data di discussione della Tesi
21 Luglio 2021

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