49 citations · 189 across the 32 of their papers we have counts for
36 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…
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…
Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach
Nicolò A. Girardini, Simone Centellegher, Andrea Passerini +3
One population group that had to significantly adapt and change their behaviour during the COVID-19 pandemic is students. While previous studies have extensively investigated the i…
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…
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…
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…
Enhancing SMT-based Weighted Model Integration by Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin +2
The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress…
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…
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Antonio Longa, Veronica Lachi, Gabriele Santin +5
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the grap…
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Antonio Longa, Steve Azzolin, Gabriele Santin +4
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting…
Global Explainability of GNNs via Logic Combination of Learned Concepts
Steve Azzolin, Antonio Longa, Pietro Barbiero +2
While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less…
SMT-based Weighted Model Integration with Structure Awareness
Giuseppe Spallitta, Gabriele Masina, Paolo Morettin +2
Weighted Model Integration (WMI) is a popular formalism aimed at unifying approaches for probabilistic inference in hybrid domains, involving logical and algebraic constraints. Des…
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
Stefano Teso, Laurens Bliek, Andrea Borghesi +4
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variab…
Lifelong Personal Context Recognition
Andrea Bontempelli, Marcelo Rodas Britez, Xiaoyue Li +5
We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the person…
Concept-level Debugging of Part-Prototype Networks
Andrea Bontempelli, Stefano Teso, Katya Tentori +2
Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets comput…
GlanceNets: Interpretabile, Leak-proof Concept-based Models
Emanuele Marconato, Andrea Passerini, Stefano Teso
There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A k…
Personalized Algorithmic Recourse with Preference Elicitation
Giovanni De Toni, Paolo Viappiani, Stefano Teso +2
Algorithmic Recourse (AR) is the problem of computing a sequence of actions that -- once performed by a user -- overturns an undesirable machine decision. It is paramount that the…
Generating fine-grained surrogate temporal networks
Antonio Longa, Giulia Cencetti, Sune Lehmann +2
Temporal networks are essential for modeling and understanding systems whose behavior varies in time, from social interactions to biological systems. Often, however, real-world dat…
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Giovanni De Toni, Bruno Lepri, Andrea Passerini
Being able to provide counterfactual interventions - sequences of actions we would have had to take for a desirable outcome to happen - is essential to explain how to change an unf…
The Science of Rejection: A Research Area for Human Computation
Burcu Sayin, Jie Yang, Andrea Passerini +1
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
Learning compositional programs with arguments and sampling
Giovanni De Toni, Luca Erculiani, Andrea Passerini
One of the most challenging goals in designing intelligent systems is empowering them with the ability to synthesize programs from data. Namely, given specific requirements in the…
An Efficient Procedure for Mining Egocentric Temporal Motifs
Antonio Longa, Giulia Cencetti, Bruno Lepri +1
Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal s…
Toward a Unified Framework for Debugging Concept-based Models
Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini +1
In this paper, we tackle interactive debugging of "gray-box" concept-based models (CBMs). These models learn task-relevant concepts appearing in the inputs and then compute a predi…