If you made any changes in Pure these will be visible here soon.

Personal profile

Research interests

I work on theoretically sound and practically useful machine learning. In particular, I aim to scale machine learning through parallelization. Since the amount of data-generating devices is growing rapidly, traditional cloud-based approaches to learning from their data become infeasible. Instead, pushing the learning onto - or close to - the devices allows to scale the learning, effectively use the device's computing power, minimize communication-overhead, and to protect privacy-sensitive data. For such parallelizations I seek theoretical guarantees on model quality, speedup, communication overhead, and privacy; at the same time I strive to provide practically useful software and tools.

I also work on the theoretical foundations of deep learning, seeking to understand the connections between the training process and the generalization abilities of neural networks. This is important not only for sound parallelizations, but in a broader sense for the interpretability and trustworthiness of deep learning.

Application domains I have worked on include machine learning for the automotive industry (e.g., autonomous driving), cybersecurity, biochemistry, and financial time-series.

Research area keywords

  • Machine Learning
  • distributed optimisation
  • Parallel Computing
  • statistical learning theory
  • Deep Learning
  • Deep Learning for Cyber Security
  • co-regularization
  • Convex Optimization
  • Online Learning

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects

Research Output

Efficient decentralized deep learning by dynamic model averaging

Kamp, M., Adilova, L., Sicking, J., Hüger, F., Schlicht, P., Wirtz, T. & Wrobel, S., 2019, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018 Dublin, Ireland, September 10–14, 2018 Proceedings, Part I. Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N. & Ifrim, G. (eds.). Cham Switzerland: Springer, p. 393-409 17 p. (Lecture Notes in Computer Science ; vol. 11051 ).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

First international workshop on data-centric dependability and security (DCDS)

Medeiros, I., Gashi, I., Kamp, M. & Ferreira, P., 2019, Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2019, Workshop Volume. Roy, M. & Huang, Y. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. IX 1 p. 8806016

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Open Access

Information-theoretic perspective of federated learning

Adilova, L., Rosenzweig, J. & Kamp, M., 2019, ITML 2019 - NeurIPS 2019 Workshop on Information Theory and Machine Learning. Zhao, S., Song, J., Choi, K., Kalluri, P., Han, Y., Jiao, J., Dimakis, A., Poole, B., Weissman, T. & Ermon, S. (eds.). Atlanta Georgia USA: Association for Information Systems, 5 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Open Access
File

System misuse detection via informed behavior clustering and modeling

Adilova, L., Natious, L., Chen, S., Thonnard, O. & Kamp, M., 2019, Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019, Workshop Volume. Roy, M. & Huang, Y. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 15-23 9 p. 8806013

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Corresponding projections for orphan screening

Giesselbach, S., Ullrich, K., Kamp, M., Paurat, D. & Gärtner, T., 2018, ML4H: Machine Learning for Health, Workshop at NeurIPs 2018. Finlayson, S. & Beaulieu-Jones, B. K. (eds.). San Diego CA USA: Neural Information Processing Systems (NIPS), 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Open Access
File