12 papers
Bi-HYCO: Bi-Objective Cooperative Learning for PDE Parameter Identification under Fragmented Observations
Umberto Biccari, Jun Chen, Roberto Morales +1
Physical and synthetic models may describe complementary aspects of the same PDE-governed system while receiving different, possibly fragmented, observations. We propose Bi-Objecti…
Interior interpretability with attention rollout: contraction and propagation profiles in Transformers
Umberto Biccari, Qian Huang, Enrique Zuazua
Feature-attribution methods assign scores relating input variables to a model's output, but do not by themselves characterize how explicitly defined interaction operators compose a…
Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3
Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environmen…
Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4
Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where al…
Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3
The recent success of large language models (LLMs) has been largely driven by vast public datasets. However, the next frontier for LLM development lies beyond public data. Much of…
Geometric Asymptotics of Score Mixing and Guidance in Diffusion Models
Kang Liu, Enrique Zuazua
Diffusion models are routinely guided in practice by combining multiple score fields, yet the mathematical structure of score mixing is still poorly understood. We study the small-…
Fair feature attribution for multi-output prediction: a Shapley-based perspective
Umberto Biccari, Alain Ibáñez de Opakua, José María Mato +3
In this article, we provide an axiomatic characterization of feature attribution for multi-output predictors within the Shapley framework. While SHAP explanations are routinely com…
Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies
Astrid Brull, Sara Aguti, Véronique Bolduc +9
The application of Machine Learning (ML) to the diagnosis of rare diseases, such as collagen VI-related dystrophies (COL6-RD), is fundamentally limited by the scarcity and fragment…
Federated Learning for Pediatric Pneumonia Detection: Enabling Collaborative Diagnosis Without Sharing Patient Data
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Joaquin Del Rio +2
Early and accurate pneumonia detection from chest X-rays (CXRs) is clinically critical to expedite treatment and isolation, reduce complications, and curb unnecessary antibiotic us…
Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Joaquin Del Rio +2
Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Train…
The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications
Alex Acero, Daniel M. Jimenez-Gutierrez, Dario Pighin +3
Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data private. There are two main paradigms in FL: Horizontal…
Machine Learning and Control: Foundations, Advances, and Perspectives
Enrique Zuazua
Control theory of dynamical systems offers a powerful framework for tackling challenges in deep neural networks and other machine learning architectures. We show that concepts such…