Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking
Bibliographic record
Abstract
Chairs' WelcomeIt is with great pleasure that we welcome you to the 2023 ACM CoNEXT Workshop on 'Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking' -SAFE'23.We are excited to be hosting the first edition of this workshop, and it brings us pleasure to see the growing interest and enthusiasm surrounding the convergence of machine learning and networking.Machine learning offers promising solutions for network optimization, security, and management.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 based solutions which are black box in nature.The production networks have a critical and sensitive nature, where outages or performance degradations can be very costly.Thus, with SAFE'23 we aim to create an engaging platform for researchers and industry experts to share their insights and experiences in this emerging field.It is a pivotal platform for fostering dialogue and collaboration among researchers, industry experts, and all those passionate about the intersection of machine learning and networking.It is here that we intend to build bridges between theory and practice, where knowledge is exchanged, and experiences are shared.Together, we can address the challenges and seize the opportunities that lie at the crossroads of machine learning and networking through Explainable and Safety Bounded, Fidelitous, Machine Learning.The call for papers attracted submissions from Asia, Canada, Europe and the United States and we accepted a total of 4 papers.We also encourage all attendees to make sure they do not miss our keynote presentation.Our distinguished keynote speaker will share invaluable insights and draw upon their extensive experience.This promises to be a highlight of the event, offering a unique opportunity to gain deep understanding and inspiration:• The Quest for Safe Deep Reinforcement Learning-driven Network Slicing: Progress, Pitfalls and Potential by Yassine Hadjadj Aoul, who is currently a Full Professor at Univ Rennes/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.167 | 0.064 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".