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MaLeNe 2021 : 1st International Workshop on Machine Learning in Networking


When Sep 13, 2021 - Sep 14, 2021
Where Lübeck, Germany
Submission Deadline May 30, 2021
Notification Due Jul 8, 2021
Final Version Due Jul 22, 2021
Categories    communication networks   computer networks   machine learning   artificial intelligence

Call For Papers

MaLeNe 2021 aims at providing an international forum for researchers addressing emerging concepts and challenges related to machine learning in networking. The workshop will aim to address opportunities where machine learning can bring benefits to networking in different facets, such as network monitoring, management, and security. Together with flexible and programmable networks this paves the way towards a more proactive and autonomous network design and “self-driving” networks. The long-term vision is that configuration decisions can be made in real-time in an automated fashion before service and experience degradation occurs. The workshop will combine original paper presentations with a motivating keynote to thoroughly explore this challenging topic.

Authors are invited to submit papers that fall into or are related to the topic areas listed below:

* Methodology
- Data sets for benchmarking, verification, proof of concept
- Data augmentation
- Performance evaluation methodology (best practices)
- Good standards for data publishing
- Data prediction and generation (e.g., GANs)
- Dimensionality reduction (e.g., autoencoder)
* Machine Learning Algorithms
- Classical methods like supervised, unsupervised, reinforcement learning
- Deep methods vs non-deep methods
- Graph neural networks
- Advanced methods like adversarial, transfer
* Generalizability
- Transfer of trained models (e.g., small to large networks, enterprise to data center)
- Federated learning (combine models trained for different data sets)
- Machine unlearning
- Catastrophic forgetting
* Explainability
- Visualization
- Understanding decisions of ML-based systems (management, traffic engineering, etc.)
- Game-theory-based approaches to approximate guarantees
* Networking for Machine Learning and AI
- Network architectures
- Network applications
- Network use cases (data center, enterprise, etc.)
- Network resource management (algorithms, schedulers, etc.)
- In-network processing
* Applications in Networking
- Network monitoring, especially from encrypted traffic (e.g., traffic classification, QoE)
- Network configuration (e.g., suggest optimal configurations, “spell-check” text-based configuration data)
- Network planning (e.g., reconfigurable data centers, job placement)
- Network management (e.g., autonomous management, self-driving networks)
- Network security (e.g., intrusion detection, covert channels, firewall)
- Advanced networks (e.g., 5G to 6G, industry, slicing)
* Hot Topics from Machine Learning
- Self-supervised learning
- Intrinsic motivation, empowerment, curiosity
- Language processing in networking
- Meta-artificial intelligence (learning to learn)


* All contributions should be submitted as PDF documents. Submissions may be up to 12 pages long (11pt font, one-column format) plus 2 pages for references. Template:
* Link to submission system: (select track: Workshop MaLeNe)

Important dates:

* Submission deadline: 30.05.2021
* Notification of acceptance: 08.07.2021
* Final submission/Camera-ready version and registration: 22.07.2021
* Workshop date: 13./14.09.2021

Workshop Co-Chairs:

* Michael Seufert, University of Würzburg
* Andreas Blenk, Technical University of Munich

Technical Program Committee:

* Luigi Atzori, University of Cagliari
* Chadi Barakat, INRIA
* Pere Barlet-Ros, Universitat Politecnica de Catalunya
* Robert Bauer, Karlsruhe Institute of Technology
* Thomas Bauschert, Technical University of Chemnitz
* Raouf Boutaba, University of Waterloo
* Laurent Ciavaglia, NOKIA Bell Labs
* Emir Halepovic, AT&T Labs - Research
* Paul Harvey, Rakuten Mobile
* Oliver Hohlfeld, Brandenburg University of Technology
* Holger Karl, University Paderborn
* Andreas Kassler, Karlstadts Universitet
* Wolfgang Kellerer, Technical University of Munich
* Stanislav Lange, NTNU
* Noura Limam, University of Waterloo
* Michael Menth, University of Tuebingen
* Amr Rizk, Universität Ulm
* Dario Rossi, Huawei
* Lea Skorin-Kapov, University of Zagreb
* Rolf Stadler, KTH Royal Institute of Technology
* Rebecca Steinert, Amazon Development Center Germany
* Mirko Suznjevic, University of Zagreb, Faculty of Electrical Engineering and Computing
* Oliver Waldhorst, Hochschule Karlsruhe - Technik und Wirtschaft
* Nur Zincir-Heywood, Dalhousie University


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