Structured Machine Learning Group
Structured Machine Learning Group
Explainable AI
We study theoretical and applied techniques for building interpretable machine learning models. We also research how to extract explanations from black-box pre-trained models (e.g., deep networks) via concepts, counterfactual explanations and interventions.
Constructive Preference Elicitation
Constructive preference elicitation is a technique for learning user preference models over combinatorial domains.
Temporal Graph Generation
Temporal networks generation is a complex task. In particular, we are working on generating temporal graphs that are statistically similar to a given temporal network.
Egocentric Temporal Motifs Miner
Egocentric Temporal Motifs Miner is a novel mining technique based on the node egocentric perspective. Egocentric Temporal Motifs are stored in a unique binary string; thus, we can verify isomorphism in linear time.
Learning Modulo Theories
Learning Modulo Theories is a machine learning framework to perform discriminative structured-output prediction over hybrid constrained domains.
Weighted Model Integration
Weighted Model Integration is a method for solving inference problems over arbitrary probabilistic models over constrained domains.