Welcome to the website of the Workshop on “Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking” to be held as a part of the CoNEXT 2023 conference, Paris, 5 - 8 December, 2023.

You will find here information about how to submit papers, how to come to the workshop, etc.

Machine learning techniques are becoming increasingly popular in the field of networking. It offers promising solutions for network optimization, security, and management. However, the lack of transparency and interpretability in machine learning models poses challenges for understanding and trusting their decisions in critical networking scenarios. Moreover, ensuring safety and reliability is of utmost importance when deploying machine learning in real-world network environments.

Control and decision-making algorithms are critical for the operation of networks, hence we believe that the solutions should be safety bounded and interpretable. Understanding the decisions and behaviors of machine learning models is crucial for optimizing network performance, enhancing security, and ensuring reliable network operations. This is a very crucial topic which needs to be addressed, as network operators, managers or administrators are reluctant to use ML in production networks because of their critical and sensitive nature, e.g., as outages and performance degradations can be very costly.

We invite original research contributions as well as position papers addressing, but not limited to, the following topics:


Submission procedure

Papers should be submitted via conext23-safe.hotcrp.com submission System.

All submitted papers will be assessed through a double-blind review process. This means that the authors do not see who are the reviewers and the reviewers do not see who are the authors.

Please see more details in the section Submission.

Important Dates


This workshop is supported by the French ANR project SAFE and the Spanish project bRAIN .

image from Call for Papers

Call for Papers

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