2 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Ivan Donadello, Anthony Hunter, Stefano Teso +1
Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the pr…
Coactive Critiquing: Elicitation of Preferences and Features
Stefano Teso, Paolo Dragone, Andrea Passerini
When faced with complex choices, users refine their own preference criteria as they explore the catalogue of options. In this paper we propose an approach to preference elicitation…
Structured Learning Modulo Theories
Stefano Teso, Roberto Sebastiani, Andrea Passerini
Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hyb…