Stefano Teso

GlanceNets: Interpretabile, Leak-proof Concept-based Models

2022 · arXiv (Cornell University)

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 key requirement is that the concepts be interpretable. Existing CBMs tackle …

Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations

2023 · arXiv (Cornell University)

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. This setup offers clear advantages in terms of consistency to symbolic prior knowledge, …

Learning to Guide Human Experts via Personalized Large Language Models

2023 · arXiv (Cornell University)

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 machine's decisions, due …

If Concept Bottleneck ARE THE QUESTION, ARE FOUNDATION MODELS THE ANSWER?

2025 · Zenodo (CERN European Organization for Nuclear Research)

Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance withante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts(e.g., visible objects and their properties) and then use these to …