most citedPolicy-driven Conformal Prediction for Trustworthy QoT Estimation

2 citations · 4 across the 8 of their papers we have counts for

collaborators

9 papers

cs.LG2026

TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction

Kiarash Rezaei, Mehdi Sattari, Javad Aliakbari +3

Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication. Each CSI frame is a matrix-valued representation of the wirel…

cs.NI2026

Radio-Optical Confluence in Intelligent Edge Networks

Akshita Gupta, Devika Dass, Agastya Raj +4

Challenges associated with densification of radio access networks are motivating exploration of more efficient and scalable architectures. We examine recent progress in one directi…

eess.SP2026

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…

cs.LG2026

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…

cs.NI2026

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…

cs.NI2026

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…