:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	De Felice, Giovanni, D'Elia, Riccardo, Termine, Alberto, Barbiero, Pietro, Marra, Giuseppe, Santini, Silvia
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.02239
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Foundations of Interpretable Models
by: Barbiero, Pietro, et al.
Published: (2025)

Causally Reliable Concept Bottleneck Models
by: De Felice, Giovanni, et al.
Published: (2025)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
by: Dominici, Gabriele, et al.
Published: (2024)

Actionable Interpretability Must Be Defined in Terms of Symmetries
by: Barbiero, Pietro, et al.
Published: (2026)

Interpretable Neural System Dynamics: Combining Deep Learning with System Dynamics Modeling to Support Critical Applications
by: D'Elia, Riccardo
Published: (2025)

Prototype-Grounded Concept Models for Verifiable Concept Alignment
by: Colamonaco, Stefano, et al.
Published: (2026)

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
by: Debot, David, et al.
Published: (2025)

Interpretable Concept-Based Memory Reasoning
by: Debot, David, et al.
Published: (2024)

Towards explainable decision support using hybrid neural models for logistic terminal automation
by: D'Elia, Riccardo, et al.
Published: (2025)

Federated Concept-Based Models: Interpretable models with distributed supervision
by: Fenoglio, Dario, et al.
Published: (2026)

Relational Concept Bottleneck Models
by: Barbiero, Pietro, et al.
Published: (2023)

Counterfactual Concept Bottleneck Models
by: Dominici, Gabriele, et al.
Published: (2024)

Neural Interpretable Reasoning
by: Barbiero, Pietro, et al.
Published: (2025)

Interpretable Neural-Symbolic Concept Reasoning
by: Barbiero, Pietro, et al.
Published: (2023)

Why Do Time Series Models Need Long Context Windows?
by: Butera, Luca, et al.
Published: (2026)

On the Regularization of Learnable Embeddings for Time Series Forecasting
by: Butera, Luca, et al.
Published: (2024)

Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models
by: De Santis, Francesco, et al.
Published: (2025)

Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
by: Debot, David, et al.
Published: (2025)

A foundation model for electrodermal activity data
by: Alchieri, Leonardo, et al.
Published: (2026)

Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)
by: Fan, Tiffany, et al.
Published: (2025)

Mixture of Concept Bottleneck Experts
by: De Santis, Francesco, et al.
Published: (2026)

KnowIt: Deep Time Series Modeling and Interpretation
by: Theunissen, M. W., et al.
Published: (2025)

AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
by: Dominici, Gabriele, et al.
Published: (2024)

Physically Interpretable Representation and Controlled Generation for Turbulence Data
by: Fan, Tiffany, et al.
Published: (2025)

Scalable Evaluation and Neural Models for Compositional Generalization
by: Camposampiero, Giacomo, et al.
Published: (2025)

If Concept Bottlenecks are the Question, are Foundation Models the Answer?
by: Debole, Nicola, et al.
Published: (2025)

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
by: Pugnana, Andrea, et al.
Published: (2025)

Counterfactual Explanations for Clustering Models
by: Spagnol, Aurora, et al.
Published: (2024)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
by: Kan, Ziwen, et al.
Published: (2024)

Relational Neurosymbolic Markov Models
by: De Smet, Lennert, et al.
Published: (2024)

Deep Time Warping for Multiple Time Series Alignment
by: Nourbakhsh, Alireza, et al.
Published: (2025)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
by: Fenoglio, Dario, et al.
Published: (2024)

Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction
by: Hu, Shiyan, et al.
Published: (2026)

Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
by: Tao, Xiaoyu, et al.
Published: (2024)

FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
by: Fenoglio, Dario, et al.
Published: (2025)

R-Tuning: Wavelet-Decomposed Replay and Semantic Alignment for Continual Adaptation of Pretrained Time-Series Models
by: Yin, Tianyi, et al.
Published: (2025)

Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators
by: Fan, Yiming, et al.
Published: (2024)

ML-based identification of the interface regions for coupling local and nonlocal models
by: Nader, Noujoud, et al.
Published: (2024)

Measuring Time Series Forecast Stability for Demand Planning
by: Klee, Steven, et al.
Published: (2025)

Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics
by: Ferfoglia, Irene, et al.
Published: (2025)