Gabriele Dominici

According to our database, Gabriele Dominici authored at least 17 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation.

Francesco Sovrano, Gabriele Dominici, Marc Langheinrich

CoRR, May, 2026

Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering.

Francesco Sovrano, Gabriele Dominici, Alberto Bacchelli

CoRR, April, 2026

Federated Concept-Based Models: Interpretable models with distributed supervision.

Dario Fenoglio, Arianna Casanova, Francesco De Santis, Mohan Li, Gabriele Dominici, Johannes Schneider, Martin Gjoreski, Marc Langheinrich, Pietro Barbiero, Giovanni de Felice

CoRR, February, 2026

2025

Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?

Teague McMillan, Gabriele Dominici, Martin Gjoreski, Marc Langheinrich

CoRR, October, 2025

Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas.

Francesco Sovrano, Gabriele Dominici, Rita Sevastjanova, Alessandra Stramiglio, Alberto Bacchelli

CoRR, August, 2025

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning.

David Debot, Pietro Barbiero, Gabriele Dominici, Giuseppe Marra

CoRR, June, 2025

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts.

Andrea Pugnana, Riccardo Massidda, Francesco Giannini, Pietro Barbiero, Mateo Espinosa Zarlenga, Roberto Pellungrini, Gabriele Dominici, Fosca Giannotti, Davide Bacciu

CoRR, March, 2025

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts.

Mateo Espinosa Zarlenga, Gabriele Dominici, Pietro Barbiero, Zohreh Shams, Mateja Jamnik

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning.

Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Counterfactual Concept Bottleneck Models.

Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Evaluating Explanations Through LLMs: Beyond Traditional User Studies.

Francesco Bombassei De Bona, Gabriele Dominici, Tim Miller, Marc Langheinrich, Martin Gjoreski

CoRR, 2024

DC3DO: Diffusion Classifier for 3D Objects.

Nursena Köprücü, Meher Shashwat Nigam, Shicheng Xu, Biruk Abere, Gabriele Dominici, Andrew Rodriguez, Sharvaree Vadgama, Berfin Inal, Alberto Tono

CoRR, 2024

Causal Concept Embedding Models: Beyond Causal Opacity in Deep Learning.

Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich

CoRR, 2024

Climbing the Ladder of Interpretability with Counterfactual Concept Bottleneck Models.

Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich

CoRR, 2024

AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model.

Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Marc Langheinrich

Proceedings of the Joint Proceedings of the xAI 2024 Late-breaking Work, 2024

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning.

Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

SHARCS: Shared Concept Space for Explainable Multimodal Learning.

Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister, Pietro Liò, Nikola Simidjievski

CoRR, 2023