1 code implementation • 22 Jun 2023 • Marco Heyden, Edouard Fouché, Vadim Arzamasov, Tanja Fenn, Florian Kalinke, Klemens Böhm
In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur.
no code implementations • 12 Jun 2023 • Marco Heyden, Vadim Arzamasov, Edouard Fouché, Klemens Böhm
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs.
no code implementations • 25 May 2022 • Florian Kalinke, Marco Heyden, Edouard Fouché, Klemens Böhm
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e. g., predictive maintenance, fraud detection, or medicine.
1 code implementation • 23 Jul 2021 • Junpei Komiyama, Edouard Fouché, Junya Honda
We demonstrate that ADR-bandit has nearly optimal performance when abrupt or gradual changes occur in a coordinated manner that we call global changes.
1 code implementation • 13 Nov 2020 • Edouard Fouché, Florian Kalinke, Klemens Böhm
In the real world, data streams are ubiquitous -- think of network traffic or sensor data.
1 code implementation • 4 Oct 2018 • Edouard Fouché, Klemens Böhm
In this paper, we propose Monte Carlo Dependency Estimation (MCDE), a theoretical framework to estimate multivariate dependency in static and dynamic data.