SAFE 2023 : Workshop on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking @CONEXT 2023 Paris, France
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Event Date: | December 05, 2023 - December 08, 2023 |
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Submission Deadline: | September 30, 2023 |
Notification of Acceptance: | October 20, 2023 |
Camera Ready Version Due: | October 25, 2023 |
Call for Papers |
This workshop will be held as a part of the CoNEXT 2023 conference from Paris, 5 - 8 December, 2023.
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: - 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 https://conext23-safe.hotcrp.com for more details please see the webpage https://safeworkshop.github.io/posts/submission/ Organising committee: - Kamal Singh, University St-Etienne, France - Abbas Bradai, University of Poitiers, France - Pham Tran Anh Quang, Huawei Technologies, France - Antonio Pescapè, University of Napoli Federico II, Italy - Claudio Fiandrino, IMDEA Networks Institute, Madrid, Spain |
Summary |
SAFE 2023 : Workshop on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking @CONEXT 2023 will take place in Paris, France. It’s a 4 days event starting on Dec 5, 2023 (Tuesday) and will be winded up on Dec 8, 2023 (Friday). SAFE 2023 falls under the following areas: EXPLAINABLE AI, MACHINE LEARNING, NETWORKING, SAFE REINFORCEMENT LEARNING, etc. Submissions for this Workshop can be made by Sep 30, 2023. Authors can expect the result of submission by Oct 20, 2023. Upon acceptance, authors should submit the final version of the manuscript on or before Oct 25, 2023 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the SAFE 2023
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Credits and Sources |
[1] SAFE 2023 : Workshop on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking @CONEXT 2023 |