2 citations · 3 across the 6 of their papers we have counts for
8 papers
Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
Ali Al Housseini, Carlos Natalino, Paolo Monti +1
We propose a retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on sourc…
Explanation-Based Runtime Verification for Trustworthy ML-driven Optical Networks
Omran Ayoub, Carlos Natalino, Ali Al Housseini +5
Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, performance monitoring and resource…
Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning
Ali Al Housseini, Carlos Natalino, Paolo Monti +1
The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks. Models trained on data from a specific topology or…
Human Grounded Evaluation of Large Language Models for Optical Network Automation
Kiarash Rezaei, Omran Ayoub, Paolo Monti +1
Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuG…
Policy-driven Conformal Prediction for Trustworthy QoT Estimation
Kiarash Rezaei, Omran Ayoub, Paolo Monti +1
We propose Conformal QoT, a policy-driven framework that combines statistically guaranteed QoT estimation with operational decision policies, enabling reliable lightpath-feasibilit…
Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
Kiarash Rezaei, Omran Ayoub, Sebastian Troia +3
As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust. Exis…