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Chapter and Conference Paper
A Remark on Concept Drift for Dependent Data
Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the streaming context usually assumi...
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Article
Open AccessContrasting Explanations for Understanding and Regularizing Model Adaptations
Many of today’s decision making systems deployed in the real world are not static—they are changing and adapting over time, a phenomenon known as model adaptation takes place. Because of their wide reaching in...
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Chapter and Conference Paper
On the Change of Decision Boundary and Loss in Learning with Concept Drift
Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as ...
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Chapter and Conference Paper
Suitability of Different Metric Choices for Concept Drift Detection
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adj...
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Chapter and Conference Paper
Taking Care of Our Drinking Water: Dealing with Sensor Faults in Water Distribution Networks
The water supply is part of the critical infrastructure as the accessibility of clean drinking water is essential to ensure the health of the people. To guarantee the availability of fresh water, efficient and...
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Chapter and Conference Paper
Contrastive Explanations for Explaining Model Adaptations
Many decision making systems deployed in the real world are not static - a phenomenon known as model adaptation takes place over time. The need for transparency and interpretability of AI-based decision models...
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Chapter and Conference Paper
Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data
Recently, machine learning techniques are often applied in real world scenarios where learning signals are provided as a stream of data points, and models need to be adapted online according to the current inf...
