Articles

Partial-Label Learning with a Reject Option
Fuchs, T.; Kalinke, F.; Böhm, K.
2025. Transactions on Machine Learning Research

Nyström Kernel Stein Discrepancy
Kalinke, F.; Szabó, Z.; Sriperumbudur, B. K.
2025. International Conference on Artificial Intelligence and Statistics (AISTATS), Mai Khao, 3rd-5th May 2025, 388–396

Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Kalinke, F.; Heyden, M.; Gntuni, G.; Fouché, E.; Böhm, K.
2025. Transactions on Machine Learning Research, 2025

The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Kalinke, F.; Szabó, Z.
2024. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Ed.: A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, C. Zhang, 108468–108489, Curran Associates, Inc

Adaptive Bernstein change detector for high-dimensional data streams
Heyden, M.; Fouché, E.; Arzamasov, V.; Fenn, T.; Kalinke, F.; Böhm, K.
2024. Data Mining and Knowledge Discovery, 38 (3), 1334–1363. doi:10.1007/s10618-023-00999-5

Multi-kernel Times Series Outlier Detection
Kalinke, F.; Fouché, E.; Thiessen, H.; Böhm, K.
2023. Discovery Science. DS 2023. Eds.: A. Bifet, A.C. Lorena, R.P. Ribeiro, J. Gama, P.H. Abreu, 688 – 702, Springer Nature Switzerland. doi:10.1007/978-3-031-45275-8_46

Nyström -Hilbert-Schmidt Independence Criterion
Kalinke, F.; Szabó, Z.
2023. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2023, 1005 – 1015, Machine Learning Research Press (ML Research Press)

Efficient subspace search in data streams
Fouché, E.; Kalinke, F.; Böhm, K.
2021. Information systems, 97, Art.-Nr. 101705. doi:10.1016/j.is.2020.101705

An Evaluation of NILM Approaches on Industrial Energy-Consumption Data
Kalinke, F.; Bielski, P.; Singh, S.; Fouché, E.; Böhm, K.
2021. e-Energy ’21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, 239–243, Association for Computing Machinery (ACM). doi:10.1145/3447555.3464863

A framework for dependency estimation in heterogeneous data streams
Fouché, E.; Mazankiewicz, A.; Kalinke, F.; Böhm, K.
2021. Distributed and parallel databases, 39, 415–444. doi:10.1007/s10619-020-07295-x

Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines
Geiger, C.; Maier, N.; Kalinke, F.; Geimer, M.
2020. Fluid Power Future Technology! Vol. 3, 83–92, Technische Universität Dresden (TU Dresden). doi:10.25368/2020.97