- RUB
- Computer Science
- INI
- People
- Dr. Michael Kamp
I am leader of the research group Trustworthy Machine Learning at the Institut für KI in der Medizin (IKIM), located at the Ruhr-University Bochum. In 2021 I was a postdoctoral researcher at the CISPA Helmholtz Center for Information Security in the Exploratory Data Analysis group of Jilles Vreeken. From 2019 to 2021 I was a postdoctoral research fellow at Monash University, where I am still an affiliated researcher. From 2011 to 2019 I was a data scientist at Fraunhofer IAIS, where I lead Fraunhofer’s part in the EU project DiSIEM, managing a small research team. Moreover, I was a project-specific consultant and researcher, e.g., for Volkswagen, DHL, and Hussel, and I designed and gave industrial trainings. Since 2014 I was simultaneously a doctoral researcher at the University of Bonn, teaching graduate labs and seminars, and supervising Master’s and Bachelor’s theses. Before that, I worked for 10 years as a software developer.
Selected Publications
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Federated Learning from Small DatasetsKamp, M., Fischer, J., & Vreeken, J.In International Conference on Learning Representations (ICLR)
@inproceedings{KampFischerVreeken2023, author = {Kamp, Michael and Fischer, Jonas and Vreeken, Jilles}, title = {Federated Learning from Small Datasets}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2023}, }Kamp, M., Fischer, J., & Vreeken, J. (2023). Federated Learning from Small Datasets. In International Conference on Learning Representations (ICLR).2021
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationLi, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q.In International Conference on Learning Representations@inproceedings{LiJiangZhangEtAl2021, author = {Li, Xiaoxiao and Jiang, Meirui and Zhang, Xiaofei and Kamp, Michael and Dou, Qi}, title = {FedBN: Federated Learning on Non-IID Features via Local Batch Normalization}, booktitle = {International Conference on Learning Representations}, year = {2021}, }Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. (2021). FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. In International Conference on Learning Representations.Relative flatness and generalizationPetzka, H., Kamp, M., Adilova, L., Sminchisescu, C., & Boley, M.Advances in neural information processing systems, 34, 18420–18432@article{PetzkaKampAdilovaEtAl2021, author = {Petzka, Henning and Kamp, Michael and Adilova, Linara and Sminchisescu, Cristian and Boley, Mario}, title = {Relative flatness and generalization}, journal = {Advances in neural information processing systems}, volume = {34}, pages = {18420–18432}, year = {2021}, }Petzka, H., Kamp, M., Adilova, L., Sminchisescu, C., & Boley, M. (2021). Relative flatness and generalization. Advances in neural information processing systems, 34, 18420–18432.-
Layer-wise linear mode connectivityAdilova, L., Andriushchenko, M., Kamp, M., Fischer, A., & Jaggi, M.In The Twelfth International Conference on Learning Representations
@inproceedings{AdilovaAndriushchenkoKampEtAl2024, author = {Adilova, Linara and Andriushchenko, Maksym and Kamp, Michael and Fischer, Asja and Jaggi, Martin}, title = {Layer-wise linear mode connectivity}, booktitle = {The Twelfth International Conference on Learning Representations}, year = {2024}, }Adilova, L., Andriushchenko, M., Kamp, M., Fischer, A., & Jaggi, M. (2024). Layer-wise linear mode connectivity. In The Twelfth International Conference on Learning Representations.Visual Computing for Autonomous DrivingChen, S., Gou, L., Kamp, M., & Sun, D.IEEE Computer Graphics and Applications, 44(3), 11–13@article{ChenGouKampEtAl2024, author = {Chen, Siming and Gou, Liang and Kamp, Michael and Sun, Dong}, title = {Visual Computing for Autonomous Driving}, journal = {IEEE Computer Graphics and Applications}, volume = {44}, number = {3}, pages = {11–13}, year = {2024}, }Chen, S., Gou, L., Kamp, M., & Sun, D. (2024). Visual Computing for Autonomous Driving. IEEE Computer Graphics and Applications, 44(3), 11–13.Landscaping Linear Mode ConnectivitySingh, S. P., Adilova, L., Kamp, M., Fischer, A., Schölkopf, B., & Hofmann, T.In ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning@inproceedings{SinghAdilovaKampEtAl2024, author = {Singh, Sidak Pal and Adilova, Linara and Kamp, Michael and Fischer, Asja and Schölkopf, Bernhard and Hofmann, Thomas}, title = {Landscaping Linear Mode Connectivity}, booktitle = {ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning}, year = {2024}, }Singh, S. P., Adilova, L., Kamp, M., Fischer, A., Schölkopf, B., & Hofmann, T. (2024). Landscaping Linear Mode Connectivity. In ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning.Orthogonal Gradient Boosting for Simpler Additive Rule EnsemblesYang, F., Le Bodic, P., Kamp, M., & Boley, M.In International Conference on Artificial Intelligence and Statistics (pp. 1117–1125) PMLR@inproceedings{YangLe BodicKampEtAl2024, author = {Yang, Fan and Le Bodic, Pierre and Kamp, Michael and Boley, Mario}, title = {Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles}, booktitle = {International Conference on Artificial Intelligence and Statistics}, pages = {1117–1125}, organization = {PMLR}, year = {2024}, }Yang, F., Le Bodic, P., Kamp, M., & Boley, M. (2024). Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles. In International Conference on Artificial Intelligence and Statistics (pp. 1117–1125). PMLR.2023
FAM: Relative Flatness Aware MinimizationAdilova, L., Abourayya, A., Li, J., Dada, A., Petzka, H., Egger, J., et al.TAGML2023@article{AdilovaAbourayyaLiEtAl2023, author = {Adilova, Linara and Abourayya, Amr and Li, Jianning and Dada, Amin and Petzka, Henning and Egger, Jan and Kleesiek, Jens and Kamp, Michael}, title = {FAM: Relative Flatness Aware Minimization}, journal = {TAGML2023}, year = {2023}, }Adilova, L., Abourayya, A., Li, J., Dada, A., Petzka, H., Egger, J., et al. (2023). FAM: Relative Flatness Aware Minimization. TAGML2023.Re-interpreting Rules InterpretabilityAdilova, L., Kamp, M., Andrienko, G., & Andrienko, N.International Journal of Data Science and Analytics@article{AdilovaKampAndrienkoEtAl2023, author = {Adilova, Linara and Kamp, Michael and Andrienko, Gennady and Andrienko, Natalia}, title = {Re-interpreting Rules Interpretability}, journal = {International Journal of Data Science and Analytics}, year = {2023}, }Adilova, L., Kamp, M., Andrienko, G., & Andrienko, N. (2023). Re-interpreting Rules Interpretability. International Journal of Data Science and Analytics.Federated Learning from Small DatasetsKamp, M., Fischer, J., & Vreeken, J.In International Conference on Learning Representations (ICLR)@inproceedings{KampFischerVreeken2023, author = {Kamp, Michael and Fischer, Jonas and Vreeken, Jilles}, title = {Federated Learning from Small Datasets}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2023}, }Kamp, M., Fischer, J., & Vreeken, J. (2023). Federated Learning from Small Datasets. In International Conference on Learning Representations (ICLR).Open-source skull reconstruction with MONAILi, J., Ferreira, A., Puladi, B., Alves, V., Kamp, M., Kim, M., et al.SoftwareX, 23, 101432@article{LiFerreiraPuladiEtAl2023, author = {Li, Jianning and Ferreira, André and Puladi, Behrus and Alves, Victor and Kamp, Michael and Kim, Moon and Nensa, Felix and Kleesiek, Jens and Ahmadi, Seyed-Ahmad and Egger, Jan}, title = {Open-source skull reconstruction with MONAI}, journal = {SoftwareX}, volume = {23}, pages = {101432}, year = {2023}, }Li, J., Ferreira, A., Puladi, B., Alves, V., Kamp, M., Kim, M., et al. (2023). Open-source skull reconstruction with MONAI. SoftwareX, 23, 101432.MedShapeNet – A Large-Scale Dataset of 3D Medical Shapes for Computer VisionLi, J., Pepe, A., Gsaxner, C., Luijten, G., Jin, Y., Ambigapathy, N., et al.@misc{LiPepeGsaxnerEtAl2023, author = {Li, Jianning and Pepe, Antonio and Gsaxner, Christina and Luijten, Gijs and Jin, Yuan and Ambigapathy, Narmada and Nasca, Enrico and Solak, Naida and Melito, Gian Marco and Vu, Viet Duc and Memon, Afaque R. and Chen, Xiaojun and Kirschke, Jan Stefan and de la Rosa, Ezequiel and Christ, Patrick Ferdinand and Li, Hongwei Bran and Ellis, David G. and Aizenberg, Michele R. and Gatidis, Sergios and Küstner, Thomas and Shusharina, Nadya and Heller, Nicholas and Andrearczyk, Vincent and Depeursinge, Adrien and Hatt, Mathieu and Sekuboyina, Anjany and Löffler, Maximilian and Liebl, Hans and Dorent, Reuben and Vercauteren, Tom and Shapey, Jonathan and Kujawa, Aaron and Cornelissen, Stefan and Langenhuizen, Patrick and Ben-Hamadou, Achraf and Rekik, Ahmed and Pujades, Sergi and Boyer, Edmond and Bolelli, Federico and Grana, Costantino and Lumetti, Luca and Salehi, Hamidreza and Ma, Jun and Zhang, Yao and Gharleghi, Ramtin and Beier, Susann and Sowmya, Arcot and Garza-Villarreal, Eduardo A. and Balducci, Thania and Angeles-Valdez, Diego and Souza, Roberto and Rittner, Leticia and Frayne, Richard and Ji, Yuanfeng and Chatterjee, Soumick and Dubost, Florian and Schreiber, Stefanie and Mattern, Hendrik and Speck, Oliver and Haehn, Daniel and John, Christoph and Nürnberger, Andreas and Pedrosa, João and Ferreira, Carlos and Aresta, Guilherme and Cunha, António and Campilho, Aurélio and Suter, Yannick and Garcia, Jose and Lalande, Alain and Audenaert, Emmanuel and Krebs, Claudia and Leeuwen, Timo Van and Vereecke, Evie and Röhrig, Rainer and Hölzle, Frank and Badeli, Vahid and Krieger, Kathrin and Gunzer, Matthias and Chen, Jianxu and Dada, Amin and Balzer, Miriam and Fragemann, Jana and Jonske, Frederic and Rempe, Moritz and Malorodov, Stanislav and Bahnsen, Fin H. and Seibold, Constantin and Jaus, Alexander and Santos, Ana Sofia and Lindo, Mariana and Ferreira, André and Alves, Victor and Kamp, Michael and Abourayya, Amr and Nensa, Felix and Hörst, Fabian and Brehmer, Alexander and Heine, Lukas and Podleska, Lars E. and Fink, Matthias A. and Keyl, Julius and Tserpes, Konstantinos and Kim, Moon-Sung and Elhabian, Shireen and Lamecker, Hans and Zukić, Dženan and Paniagua, Beatriz and Wachinger, Christian and Urschler, Martin and Duong, Luc and Wasserthal, Jakob and Hoyer, Peter F. and Basu, Oliver and Maal, Thomas and Witjes, Max J. H. and Chang, Ti-chiun and Ahmadi, Seyed-Ahmad and Luo, Ping and Menze, Bjoern and Reyes, Mauricio and Davatzikos, Christos and Puladi, Behrus and Kleesiek, Jens and Egger, Jan}, title = {MedShapeNet – A Large-Scale Dataset of 3D Medical Shapes for Computer Vision}, year = {2023}, }Li, J., Pepe, A., Gsaxner, C., Luijten, G., Jin, Y., Ambigapathy, N., et al. (2023). MedShapeNet – A Large-Scale Dataset of 3D Medical Shapes for Computer Vision.Nothing but Regrets - Privacy-Preserving Federated Causal DiscoveryMian, O., Kaltenpoth, D., Kamp, M., & Vreeken, J.In International Conference on Artificial Intelligence and Statistics (AISTATS)@inproceedings{MianKaltenpothKampEtAl2023, author = {Mian, Osman and Kaltenpoth, David and Kamp, Michael and Vreeken, Jilles}, title = {Nothing but Regrets - Privacy-Preserving Federated Causal Discovery}, booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2023}, }Mian, O., Kaltenpoth, D., Kamp, M., & Vreeken, J. (2023). Nothing but Regrets - Privacy-Preserving Federated Causal Discovery. In International Conference on Artificial Intelligence and Statistics (AISTATS).Information-Theoretic Causal Discovery and Intervention Detection over Multiple EnvironmentsMian, O., Kamp, M., & Vreeken, J.In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)@inproceedings{MianKampVreeken2023, author = {Mian, Osman and Kamp, Michael and Vreeken, Jilles}, title = {Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, year = {2023}, }Mian, O., Kamp, M., & Vreeken, J. (2023). Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).2022
AIMHI: Protecting Sensitive Data through Federated Co-TrainingAbourayya, A., Kamp, M., Ayday, E., Kleesiek, J., Rao, K., Webb, G. I., & Rao, B.In FL-NeurIPS 2022)@inproceedings{AbourayyaKampAydayEtAl2022, author = {Abourayya, Amr and Kamp, Michael and Ayday, Erman and Kleesiek, Jens and Rao, Kanishka and Webb, Geoffrey I. and Rao, Bharat}, title = {AIMHI: Protecting Sensitive Data through Federated Co-Training}, booktitle = {FL-NeurIPS 2022)}, year = {2022}, }Abourayya, A., Kamp, M., Ayday, E., Kleesiek, J., Rao, K., Webb, G. I., & Rao, B. (2022). AIMHI: Protecting Sensitive Data through Federated Co-Training. In FL-NeurIPS 2022).Open-Source Skull Reconstruction with MONAILi, J., Ferreira, A., Puladi, B., Alves, V., Kamp, M., Kim, M. -S., et al.arXiv preprint arXiv:2211.14051@article{LiFerreiraPuladiEtAl2022, author = {Li, Jianning and Ferreira, André and Puladi, Behrus and Alves, Victor and Kamp, Michael and Kim, Moon-Sung and Nensa, Felix and Kleesiek, Jens and Ahmadi, Seyed-Ahmad and Egger, Jan}, title = {Open-Source Skull Reconstruction with MONAI}, journal = {arXiv preprint arXiv:2211.14051}, year = {2022}, }Li, J., Ferreira, A., Puladi, B., Alves, V., Kamp, M., Kim, M. -S., et al. (2022). Open-Source Skull Reconstruction with MONAI. arXiv preprint arXiv:2211.14051.Regret-based Federated Causal DiscoveryMian, O., Kaltenpoth, D., & Kamp, M.In The KDD′22 Workshop on Causal Discovery (pp. 61–69) PMLR@inproceedings{MianKaltenpothKamp2022, author = {Mian, Osman and Kaltenpoth, David and Kamp, Michael}, title = {Regret-based Federated Causal Discovery}, booktitle = {The KDD′22 Workshop on Causal Discovery}, pages = {61–69}, organization = {PMLR}, year = {2022}, }Mian, O., Kaltenpoth, D., & Kamp, M.. (2022). Regret-based Federated Causal Discovery. In The KDD′22 Workshop on Causal Discovery (pp. 61–69). PMLR.When, Where and How does it fail? A Spatial-temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous DrivingWang, J., Li, Y., Zhou, Z., Wang, C., Hou, Y., Zhang, L., et al.IEEE Transactions on Visualization and Computer Graphics@article{WangLiZhouEtAl2022, author = {Wang, Junhong and Li, Yun and Zhou, Zhaoyu and Wang, Chengshun and Hou, Yijie and Zhang, Li and Xue, Xiangyang and Kamp, Michael and Zhang, Xiaolong and Chen, Siming}, title = {When, Where and How does it fail? A Spatial-temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2022}, }Wang, J., Li, Y., Zhou, Z., Wang, C., Hou, Y., Zhang, L., et al. (2022). When, Where and How does it fail? A Spatial-temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving. IEEE Transactions on Visualization and Computer Graphics.2021
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationLi, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q.In International Conference on Learning Representations@inproceedings{LiJiangZhangEtAl2021, author = {Li, Xiaoxiao and Jiang, Meirui and Zhang, Xiaofei and Kamp, Michael and Dou, Qi}, title = {FedBN: Federated Learning on Non-IID Features via Local Batch Normalization}, booktitle = {International Conference on Learning Representations}, year = {2021}, }Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. (2021). FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. In International Conference on Learning Representations.Relative flatness and generalizationPetzka, H., Kamp, M., Adilova, L., Sminchisescu, C., & Boley, M.Advances in neural information processing systems, 34, 18420–18432@article{PetzkaKampAdilovaEtAl2021, author = {Petzka, Henning and Kamp, Michael and Adilova, Linara and Sminchisescu, Cristian and Boley, Mario}, title = {Relative flatness and generalization}, journal = {Advances in neural information processing systems}, volume = {34}, pages = {18420–18432}, year = {2021}, }Petzka, H., Kamp, M., Adilova, L., Sminchisescu, C., & Boley, M. (2021). Relative flatness and generalization. Advances in neural information processing systems, 34, 18420–18432.The Institut für Neuroinformatik (INI) is a research unit of the Faculty of Computer Science at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.
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2024