In-database analytics with ibmdbpy. - Edouard Fouché; Alexander Eckert; Klemens Böhm (SSDBM, 2018)

Unsupervised Artificial Neural Networks for Outlier Detection in High-Dimensional Data. - Daniel Popovic; Edouard Fouché; Klemens Böhm (ADBIS, 2019)

In-Database Geospatial Analytics using Python. - Avipsa Roy; Edouard Fouché; Rafael Rodriguez Morales; Gregor Möhler (ARIC@SIGSPATIAL, 2019)

Scaling Multi-Armed Bandit Algorithms. - Edouard Fouché; Junpei Komiyama; Klemens Böhm (KDD, 2019)

Monte Carlo Dependency Estimation. - Edouard Fouché; Klemens Böhm (SSDBM, 2019)

Reducing energy time series for energy system models via self-organizing maps. - Hasan Ümitcan Yilmaz; Edouard Fouché; Thomas Dengiz; Lucas Krauß; Dogan Keles; Wolf Fichtner (it Inf. Technol., 2019)

Mining Text Outliers in Document Directories. - Edouard Fouché; Yu Meng 001; Fang Guo; Honglei Zhuang; Klemens Böhm; Jiawei Han 001 (ICDM, 2020)

Estimating Dependency, Monitoring and Knowledge Discovery in High-Dimensional Data Streams. - Edouard Fouché (2020)

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)

Tandem Outlier Detectors for Decentralized Data. - Marco Heyden; Jürgen Wilwer; Edouard Fouché; Steffen Thoma; Sven Matthiesen; Thomas Gwosch (SSDBM, 2022)

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)

MicroPPO: Safe Power Flow Management in Decentralized Micro-Grids with Proximal Policy Optimization. - Daniel Ebi; Edouard Fouché; Marco Heyden; Klemens Böhm (DSAA, 2024)

Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals. - Marco Heyden; Vadim Arzamasov; Edouard Fouché; Klemens Böhm (KDD, 2024)

DEAL: Data-Efficient Active Learning for Regression Under Drift. - Béla H. Böhnke; Edouard Fouché; Klemens Böhm (PAKDD (6), 2024)

Leveraging Plasticity in Incremental Decision Trees. - Marco Heyden; Heitor Murilo Gomes; Edouard Fouché; Bernhard Pfahringer; Klemens Böhm (ECML/PKDD (5), 2024)

Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits. - Junpei Komiyama; Edouard Fouché; Junya Honda (J. Mach. Learn. Res., 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)

The Brownian Integral Kernel: A New Kernel for Modeling Integrated Brownian Motions. - Béla H. Böhnke; Edouard Fouché; Klemens Böhm (PAKDD (6), 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)