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F. Kalinke and Z. Szabó. The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels. Technical Report, 2024.
paper (arXiv)

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


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


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


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


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

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