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:

Topics

• Explainable machine learning models for network performance optimization
• Interpretable anomaly detection and intrusion detection in networking systems
• Safety considerations and techniques for robust and reliable machine learning in networking
• Fairness, accountability, and transparency in machine learning for networking
• Hybrid models which combine formal methods and AI for explainability
• Explainable reinforcement learning for networking
• Explainable deep reinforcement learning for networking
• Safety bounded reinforcement learning for networking
• Explainable Graph neural networks for networking
• Explainable sequential decision-making
• Constraints-based explanations for networking
• Visualizations and tools for understanding and interpreting machine learning models in networking
• Case studies and real-world applications of explainable and safety bounded machine learning in networking
• Evaluation methods for explainable machine learning
• Fidelity of explainable machine learning methods

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

• Submission due: 9 September 30 September (extended firm), 2023 (AoE)
• Notification of acceptance: 15 October 20 October, 2023
• Camera-ready papers due: 25 October, 2023

Acknowledgement

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