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Change Detection for Local Explainability in Evolving Data Streams
Johannes Haug, Alexander Braun, Stefan Zürn +1
As complex machine learning models are increasingly used in sensitive applications like banking, trading or credit scoring, there is a growing demand for reliable explanation mecha…
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
Johannes Haug, Effi Tramountani, Gjergji Kasneci
Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods und…
Dynamic Model Tree for Interpretable Data Stream Learning
Johannes Haug, Klaus Broelemann, Gjergji Kasneci
Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine lear…
Deep Neural Networks and Tabular Data: A Survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler +3
Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, dee…
On Baselines for Local Feature Attributions
Johannes Haug, Stefan Zürn, Peter El-Jiz +1
High-performing predictive models, such as neural nets, usually operate as black boxes, which raises serious concerns about their interpretability. Local feature attribution method…
Learning Parameter Distributions to Detect Concept Drift in Data Streams
Johannes Haug, Gjergji Kasneci
Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distri…
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Johannes Haug, Martin Pawelczyk, Klaus Broelemann +1
Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need…