An Evaluation of NILM Approaches on Industrial Energy-Consumption Data. - Florian Kalinke; Pawel Bielski; Snigdha Singh; Edouard Fouché; Klemens Böhm (e-Energy, 2021)

A framework for dependency estimation in heterogeneous data streams. - Edouard Fouché; Alan Mazankiewicz; Florian Kalinke; Klemens Böhm (Distributed Parallel Databases, 2021)

Efficient subspace search in data streams. - Edouard Fouché; Florian Kalinke; Klemens Böhm (Inf. Syst., 2021)

Scalable Online Change Detection for High-dimensional Data Streams. - Florian Kalinke; Marco Heyden; Edouard Fouché; Klemens Böhm (CoRR, 2022)

Multi-kernel Times Series Outlier Detection. - Florian Kalinke; Edouard Fouché; Haiko Thiessen; Klemens Böhm (DS, 2023)

Nyström M-Hilbert-Schmidt independence criterion. - Florian Kalinke; Zoltán Szabó 001 (UAI, 2023)

The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels. - Florian Kalinke; Zoltán Szabó 001 (NeurIPS, 2024)

Uncertainty-Aware Partial-Label Learning. - Tobias Fuchs; Florian Kalinke; Klemens Böhm (CoRR, 2024)

Adaptive Bernstein change detector for high-dimensional data streams. - Marco Heyden; Edouard Fouché; Vadim Arzamasov; Tanja Fenn; Florian Kalinke; Klemens Böhm (Data Min. Knowl. Discov., 2024)

Nyström Kernel Stein Discrepancy. - Florian Kalinke; Zoltán Szabó 001; Bharath K. Sriperumbudur (AISTATS, 2025)

Partial-Label Learning with Conformal Candidate Cleaning. - Tobias Fuchs; Florian Kalinke (UAI, 2025)

Partial-Label Learning with a Reject Option. - Tobias Fuchs; Florian Kalinke; Klemens Böhm (Trans. Mach. Learn. Res., 2025)

Maximum Mean Discrepancy on Exponential Windows for Online Change Detection. - Florian Kalinke; Marco Heyden; Georg Gntuni; Edouard Fouché; Klemens Böhm (Trans. Mach. Learn. Res., 2025)

Optimal Online Change Detection via Random Fourier Features. - Florian Kalinke; Shakeel Gavioli-Akilagun (CoRR, 2025)

Robust partial-label learning by leveraging class activation values. - Tobias Fuchs; Florian Kalinke (Mach. Learn., 2025)