Publications
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The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
Pietro Barbiero, Giovanni De Felice, Mateo Espinosa Zarlenga, Francesco Giannini, Filippo Bonchi, Mateja Jamnik, Giuseppe Marra, Ruggero NorisarXiv 2026
Why Do Time Series Models Need Long Context Windows?
Luca Butera, Giovanni De Felice, Andrea Cini, Cesare AlippiarXiv 2026
Interpretability in Deep Time Series Models Demands Semantic Alignment
Giovanni De Felice*, Riccardo D'Elia*, Alberto Termine, Pietro Barbiero, Giuseppe Marra, Silvia SantiniICML 2026
A foundation model for electrodermal activity data
Leonardo Alchieri, Matteo Garzon, Lidia Alecci, Francesco Bombassei De Bona, Martin Gjoreski, Giovanni De Felice, Silvia SantiniarXiv 2026
Mixture of Concept Bottleneck Experts
Francesco De Santis, Gabriele Ciravegna, Giovanni De Felice, Arianna Casanova, Francesco Giannini, Michelangelo Diligenti, Johannes Schneider, Danilo Giordano, Mateo Espinosa Zarlenga, Pietro BarbieroICML 2026 (spotlight)
Federated Concept-Based Models: Interpretable models with distributed supervision
Dario Fenoglio, Arianna Casanova, Francesco De Santis, Gabriele Dominici, Johannes Schneider, Pietro Barbiero, Giovanni De Felice, Marc Langheinrich, Martin GjoreskiarXiv 2026
Causally Reliable Concept Bottleneck Models
Giovanni De Felice*, Arianna Casanova*, Francesco De Santis*, Silvia Santini, Johannes Schneider, Pietro Barbiero, Alberto TermineNeurIPS 2025
On the Regularization of Learnable Embeddings for Time Series Forecasting
Luca Butera, Giovanni De Felice, Andrea Cini, Cesare AlippiTMLR 2025
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observation
Giovanni De Felice, Andrea Cini, Daniele Zambon, Vladimir Gusev, Cesare AlippiICLR 2024
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings
Giovanni De Felice, John Goulermas, Vladimir GusevNeurIPS 2023
Spatio-Temporal Weathering Predictions in the Sparse Data Regime with Gaussian Processes
Giovanni De Felice, Vladimir Gusev, John Goulermas, Michael Gaultois, Matthew Rosseinsky, Catherine Vincent GauvinAI4Science NeurIPS 2022
Investigating extrapolation and low-data challenges via contrastive learning of chemical compositions
Federico Ottomano, Giovanni De Felice, Rahul Savani, Vladimir Gusev, Vladimir Gusev, Matthew RosseinskyAI4Mat NeurIPS 2023
Not as simple as we thought: A rigorous examination of data aggregation in materials informatics
Federico Ottomano*, Giovanni De Felice*, Vladimir Gusev, Taylor SparksDigital Discovery
Mu2e run I sensitivity projections for the neutrinoless μ−→ e− conversion search in aluminum
Mu2e CollaborationUniverse
*Equal contribution.