Alberto Termine — accepted AI-conference papers

2026

Position: Interpretability in Deep Time Series Models Demands Semantic Alignment

Giovanni De Felice, Riccardo D`Elia, Alberto Termine, Pietro Barbiero, Giuseppe Marra, Silvia Santini

ICML 2026poster

Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, existing interpretability approaches in the field keep focusing on explaining the internal model computations, without addressing whether they align or not…

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2025

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra +1

ICLR 2025poster

Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to rely on and verify state-of-the-art DNN-based systems, especially in high-stakes scenarios. For this reason, circumventing…

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2025

Causally Reliable Concept Bottleneck Models

Giovanni De Felice, Arianna Casanova, Francesco De Santis, Silvia Santini, Johannes Schneider, Pietro Barbiero +1

NeurIPS 2025poster

Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable variables, facilitating explainability and human interaction. However, these architectures, on par with popular opaque neural models, fail to account for the tr…

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