3 citations · 7 across the 9 of their papers we have counts for
9 papers
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…
Meta-Path Learning for Multi-relational Graph Neural Networks
Francesco Ferrini, Antonio Longa, Andrea Passerini +1
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or th…
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…
Egocentric Hierarchical Visual Semantics
Luca Erculiani, Andrea Bontempelli, Andrea Passerini +1
We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow…
Interval Logic Tensor Networks
Samy Badreddine, Gianluca Apriceno, Andrea Passerini +1
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of r…
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…