22 citations · 138 across the 48 of their papers we have counts for
6 papers · 1 filter
Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning
Emanuele Marconato, Andrea Passerini, Stefano Teso
Focus in Explainable AI is shifting from explanations defined in terms of low-level elements, such as input features, to explanations encoded in terms of interpretable concepts lea…
How Faithful are Self-Explainable GNNs?
Marc Christiansen, Lea Villadsen, Zhiqiang Zhong +2
Self-explainable deep neural networks are a recent class of models that can output ante-hoc local explanations that are faithful to the model's reasoning, and as such represent a s…
Learning to Guide Human Experts via Personalized Large Language Models
Debodeep Banerjee, Stefano Teso, Andrea Passerini
In learning to defer, a predictor identifies risky decisions and defers them to a human expert. One key issue with this setup is that the expert may end up over-relying on the mach…
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Emanuele Marconato, Stefano Teso, Antonio Vergari +1
Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labe…
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations
Emanuele Marconato, Stefano Teso, Andrea Passerini
Neuro-symbolic predictors learn a mapping from sub-symbolic inputs to higher-level concepts and then carry out (probabilistic) logical inference on this intermediate representation…
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra +3
We introduce Neuro-Symbolic Continual Learning, where a model has to solve a sequence of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to high-level concepts and…