Beating Human Analysts in Nowcasting Corporate Earnings by Using Publicly Available Stock Price and Correlation Features. - Michael Kamp; Mario Boley; Thomas Gärtner 001 (ICDM Workshops, 2013)

Privacy-Preserving Mobility Monitoring Using Sketches of Stationary Sensor Readings. - Michael Kamp; Christine Kopp; Michael Mock; Mario Boley; Michael May 001 (ECML/PKDD (3), 2013)

Communication-Efficient Distributed Online Prediction using Dynamic Model Synchronizations. - Mario Boley; Michael Kamp; Daniel Keren; Assaf Schuster; Izchak Sharfman (BD3@VLDB, 2013)

Communication-Efficient Distributed Online Prediction by Dynamic Model Synchronization. - Michael Kamp; Mario Boley; Daniel Keren; Assaf Schuster; Izchak Sharfman (ECML/PKDD (1), 2014)

Ligand-Based Virtual Screening with Co-regularised Support Vector Regression. - Katrin Ullrich; Michael Kamp; Thomas Gärtner 001; Martin Vogt 001; Stefan Wrobel (ICDM Workshops, 2016)

Communication-Efficient Distributed Online Learning with Kernels. - Michael Kamp; Sebastian Bothe; Mario Boley; Michael Mock (ECML/PKDD (2), 2016)

Effective Parallelisation for Machine Learning. - Michael Kamp; Mario Boley; Olana Missura; Thomas Gärtner 001 (NIPS, 2017)

Co-Regularised Support Vector Regression. - Katrin Ullrich; Michael Kamp; Thomas Gärtner 001; Martin Vogt 001; Stefan Wrobel (ECML/PKDD (2), 2017)

Issues in complex event processing: Status and prospects in the Big Data era. - Ioannis Flouris; Nikos Giatrakos; Antonios Deligiannakis; Minos N. Garofalakis; Michael Kamp; Michael Mock (J. Syst. Softw., 2017)

Efficient Decentralized Deep Learning by Dynamic Model Averaging. - Michael Kamp; Linara Adilova; Joachim Sicking; Fabian Hüger; Peter Schlicht; Tim Wirtz; Stefan Wrobel (ECML/PKDD (1), 2018)

Corresponding Projections for Orphan Screening. - Sven Giesselbach; Katrin Ullrich; Michael Kamp; Daniel Paurat; Thomas Gärtner 001 (CoRR, 2018)

System Misuse Detection Via Informed Behavior Clustering and Modeling. - Linara Adilova; Livin Natious; Siming Chen 001; Olivier Thonnard; Michael Kamp (DSN Workshops, 2019)

Information-Theoretic Perspective of Federated Learning. - Linara Adilova; Julia Rosenzweig; Michael Kamp (CoRR, 2019)

Adaptive Communication Bounds for Distributed Online Learning. - Michael Kamp; Mario Boley; Michael Mock; Daniel Keren; Assaf Schuster; Izchak Sharfman (CoRR, 2019)

A Reparameterization-Invariant Flatness Measure for Deep Neural Networks. - Henning Petzka; Linara Adilova; Michael Kamp; Cristian Sminchisescu (CoRR, 2019)

Black-Box Parallelization for Machine Learning. - Michael Kamp (2019)

Second International Workshop on Data-Centric Dependability and Security (DCDS). - Ibéria Medeiros; Ilir Gashi; Michael Kamp; Pedro Ferreira 005 (DSN Workshops, 2020)

HOPS: Probabilistic Subtree Mining for Small and Large Graphs. - Pascal Welke; Florian Seiffarth; Michael Kamp; Stefan Wrobel (KDD, 2020)

Resource-Constrained On-Device Learning by Dynamic Averaging. - Lukas Heppe; Michael Kamp; Linara Adilova; Danny Heinrich; Nico Piatkowski; Katharina Morik (PKDD/ECML Workshops, 2020)

Feature-Robustness, Flatness and Generalization Error for Deep Neural Networks. - Henning Petzka; Linara Adilova; Michael Kamp; Cristian Sminchisescu (CoRR, 2020)

Third International Workshop on Data-Centric Dependability and Security (DCDS). - Ibéria Medeiros; Ilir Gashi; Michael Kamp; Pedro Ferreira 005 (DSN Workshops, 2021)

FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. - Xiaoxiao Li; Meirui Jiang; Xiaofei Zhang; Michael Kamp; Qi Dou 001 (ICLR, 2021)

Relative Flatness and Generalization. - Henning Petzka; Michael Kamp; Linara Adilova; Cristian Sminchisescu; Mario Boley (NeurIPS, 2021)

Approaches to Uncertainty Quantification in Federated Deep Learning. - Florian Linsner; Linara Adilova; Sina Däubener; Michael Kamp; Asja Fischer (PKDD/ECML Workshops (1), 2021)

Novelty Detection in Sequential Data by Informed Clustering and Modeling. - Linara Adilova; Siming Chen 001; Michael Kamp (CoRR, 2021)

TsmoBN: Interventional Generalization for Unseen Clients in Federated Learning. - Meirui Jiang; Xiaofei Zhang; Michael Kamp; Xiaoxiao Li; Qi Dou 001 (CoRR, 2021)

Regret-based Federated Causal Discovery. - Osman Mian; David Kaltenpoth; Michael Kamp (CD@KDD, 2022)

Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments. - Osman Mian; Michael Kamp; Jilles Vreeken (AAAI, 2023)

Nothing but Regrets - Privacy-Preserving Federated Causal Discovery. - Osman Mian; David Kaltenpoth; Michael Kamp; Jilles Vreeken (AISTATS, 2023)

Federated Learning from Small Datasets. - Michael Kamp; Jonas Fischer; Jilles Vreeken (ICLR, 2023)

FAM: Relative Flatness Aware Minimization. - Linara Adilova; Amr Abourayya; Jianning Li 002; Amin Dada; Henning Petzka; Jan Egger; Jens Kleesiek; Michael Kamp (TAG-ML, 2023)

Protecting Sensitive Data through Federated Co-Training. - Amr Abourayya; Jens Kleesiek; Kanishka Rao; Erman Ayday; Bharat Rao; Geoffrey I. Webb; Michael Kamp (CoRR, 2023)

Open-source skull reconstruction with MONAI. - Jianning Li 002; André Ferreira; Behrus Puladi; Victor Alves; Michael Kamp; Moon S. Kim 002; Felix Nensa; Jens Kleesiek; Seyed-Ahmad Ahmadi; Jan Egger (SoftwareX, 2023)

When, Where and How Does it Fail? A Spatial-Temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving. - Junhong Wang; Yun Li; Zhaoyu Zhou; Chengshun Wang; Yijie Hou; Li Zhang 040; Xiangyang Xue 001; Michael Kamp; Xiaolong Luke Zhang; Siming Chen 001 (IEEE Trans. Vis. Comput. Graph., 2023)

Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles. - Fan Yang; Pierre Le Bodic; Michael Kamp; Mario Boley (AISTATS, 2024)

Layer-wise linear mode connectivity. - Linara Adilova; Maksym Andriushchenko; Michael Kamp; Asja Fischer; Martin Jaggi (ICLR, 2024)

Visual Computing for Autonomous Driving. - Siming Chen 001; Liang Gou; Michael Kamp; Dong Sun (IEEE Computer Graphics and Applications, 2024)

The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective. - Nils Philipp Walter; Linara Adilova; Jilles Vreeken; Michael Kamp (CoRR, 2024)

Landscaping Linear Mode Connectivity. - Sidak Pal Singh; Linara Adilova; Michael Kamp; Asja Fischer; Bernhard Schölkopf; Thomas Hofmann 001 (CoRR, 2024)

Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning. - Amr Abourayya; Jens Kleesiek; Kanishka Rao; Erman Ayday; Bharat Rao; Geoffrey I. Webb; Michael Kamp (AAAI, 2025)

Federated Binary Matrix Factorization Using Proximal Optimization. - Sebastian Dalleiger; Jilles Vreeken; Michael Kamp (AAAI, 2025)

Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries. - Frederic Jonske; Moon S. Kim 002; Enrico Nasca; Janis Evers; Johannes Haubold; René Hosch; Felix Nensa; Michael Kamp; Constantin Seibold; Jan Egger; Jens Kleesiek (Comput. Methods Programs Biomed., 2025)

Re-interpreting rules interpretability. - Linara Adilova; Michael Kamp; Gennady L. Andrienko; Natalia V. Andrienko (Int. J. Data Sci. Anal., 2025)

Who Should I Listen To? Adaptive Collaboration in Personalized Federated Learning. - Amr Abourayya; Jens Kleesiek; Bharat Rao; Michael Kamp (CoRR, 2025)