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AI framework boosts optical network reliability with guaranteed uncertainty bands

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Conformal Risk Control (CRC) framework to enhance the reliability of AI/ML regression models used in optical networks. This post-hoc method provides statistically guaranteed uncertainty bands for predictions without retraining existing models. The framework was applied to Quality of Transmission (QoT)-aware lightpath feasibility assessment, demonstrating significant improvements in decision accuracy under domain shifts caused by network upgrades. Experiments showed the CRC framework met its target coverage guarantee, outperforming a baseline quantile regression method and approaching transfer learning performance while avoiding retraining. AI

IMPACT Enhances reliability of AI/ML in optical networks, improving decision-making and efficiency under domain shifts.

RANK_REASON Academic paper detailing a new framework for AI/ML regression models in optical networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI framework boosts optical network reliability with guaranteed uncertainty bands

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Academic paper detailing a new framework for AI/ML regression models in optical networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kiarash Rezaei, Omran Ayoub, Paolo Monti, Carlos Natalino ·

    Conformal Risk Control for QoT-Aware Lightpath Feasibility Under Technology-Driven Domain Shift

    arXiv:2611.13893v1 Announce Type: new Abstract: Artificial intelligence and machine learning (AI/ML)-based regression models support operational decision-making in optical networks but typically expose only point predictions, which carry no statistically controlled information ab…

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