Preprints

F. Kalinke, Z. Szabó and B. K. Sriperumbudur. Nyström Kernel Stein Discrepancy. Technical Report, 2024.
paper (arXiv) | code

T. Fuchs, F. Kalinke and K. Böhm. Uncertainty-Aware Partial-Label Learning. Technical Report, 2024.
paper (arXiv) | code

F. Kalinke, M. Heyden, E. Fouché and K. Böhm. Maximum Mean Discrepancy on Exponential Windows for Online Change Detection. Technical Report, 2023.
paper (arXiv) | code

2024

F. Kalinke and Z. Szabó. The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels. Advances in Neural Information Processing Systems (NeurIPS), 2024. To appear.
paper (arXiv)

M. Heyden, E. Fouché, V. Arzamasov, T. Fenn, F. Kalinke and K. Böhm. Adaptive Bernstein Change Detector for High-Dimensional Data Streams. Data Mining and Knowledge Discovery (2024): 1-30.
paper (author version) | paper (Springer) | paper (arXiv) | code

2023

F. Kalinke and Z. Szabó. Nyström M-Hilbert-Schmidt Independence Criterion. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 216:1005-1015, 2023.
paper (UAI) | supplement (UAI) | poster | slides | paper (arXiv) | code

F. Kalinke, E. Fouché, H. Thiessen and K. Böhm. Multi-kernel Times Series Outlier Detection. Proceedings of the Twenty-Sixth International Conference on Discovery Science (DS 2023), LNAI 14276:688-702, 2023.
paper (author version) | paper (LNAI) | slides | code

2021

F. Kalinke, P. Bielski, S. Singh, E. Fouché and K. Böhm. An Evaluation of NILM Approaches on Industrial Energy-Consumption Data. e-Energy 2021: 239-243.
paper | data

E. Fouché, F. Kalinke and K. Böhm. Efficient subspace search in data streams. Information Systems 97 (2021).
paper | code

2020

E. Fouché, A. Mazankiewicz, F. Kalinke and K. Böhm. A framework for dependency estimation in heterogeneous data streams. Distributed and Parallel Databases (2020).
paper | code