Valerie Vaquet

Neurocomputing, 2024

One or two things we know about concept drift - a survey on monitoring in evolving environments. Part A: detecting concept drift.

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[DOI]

Frontiers Artif. Intell., 2024

Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations.

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[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2024

A Remark on Concept Drift for Dependent Data.

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[DOI]

Proceedings of the Advances in Intelligent Data Analysis XXII, 2024

Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks.

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[DOI]

Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, 2024

Challenges, Methods, Data-A Survey of Machine Learning in Water Distribution Networks.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023

Contrasting Explanations for Understanding and Regularizing Model Adaptations.

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[DOI]

Neural Process. Lett., October, 2023

Model-based explanations of concept drift.

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[DOI]

Neurocomputing, October, 2023

Localization of Small Leakages in Water Distribution Networks using Concept Drift Explanation Methods.

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[DOI]

CoRR, 2023

One or Two Things We know about Concept Drift - A Survey on Monitoring Evolving Environments.

[BibT_eX]

[DOI]

CoRR, 2023

Combining self-labeling and demand based active learning for non-stationary data streams.

[BibT_eX]

[DOI]

CoRR, 2023

A Sensor Fault Detection and Imputation Framework for Electrical Distribution Grids.

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[DOI]

Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2023

On the Change of Decision Boundary and Loss in Learning with Concept Drift.

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[DOI]

Proceedings of the Advances in Intelligent Data Analysis XXI, 2023

On the Hardness and Necessity of Supervised Concept Drift Detection.

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[DOI]

Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 2023

Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts.

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[DOI]

Johannes Brinkrolf

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Investigating intensity and transversal drift in hyperspectral imaging data.

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[DOI]

Neurocomputing, 2022

On the Change of Decision Boundaries and Loss in Learning with Concept Drift.

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[DOI]

CoRR, 2022

Precise Change Point Detection using Spectral Drift Detection.

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[DOI]

CoRR, 2022

Localization of Concept Drift: Identifying the Drifting Datapoints.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Suitability of Different Metric Choices for Concept Drift Detection.

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[DOI]

Proceedings of the Advances in Intelligent Data Analysis XX, 2022

Taking Care of Our Drinking Water: Dealing with Sensor Faults in Water Distribution Networks.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

From hyperspectral to multispectral sensing - from simulation to reality: A comprehensive approach for calibration model transfer.

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[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Contrasting Explanation of Concept Drift.

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[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Federated learning vector quantization for dealing with drift between nodes.

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[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings.

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[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Fast Non-Parametric Conditional Density Estimation using Moment Trees.

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[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

A Shape-Based Method for Concept Drift Detection and Signal Denoising.

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[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Evaluating Robustness of Counterfactual Explanations.

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[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Contrastive Explanations for Explaining Model Adaptations.

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[DOI]

Proceedings of the Advances in Computational Intelligence, 2021

2020

Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data.

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