Fabian Hinder

Neurocomputing, 2024

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

[BibT_eX]

[DOI]

Frontiers Artif. Intell., 2024

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

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2024

A Remark on Concept Drift for Dependent Data.

[BibT_eX]

[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.

[BibT_eX]

[DOI]

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

Semantic Properties of Cosine Based Bias Scores for Word Embeddings.

[BibT_eX]

[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.

[BibT_eX]

[DOI]

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

2023

Contrasting Explanations for Understanding and Regularizing Model Adaptations.

[BibT_eX]

[DOI]

Neural Process. Lett., October, 2023

Model-based explanations of concept drift.

[BibT_eX]

[DOI]

Neurocomputing, October, 2023

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

[BibT_eX]

[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

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XXI, 2023

On the Hardness and Necessity of Supervised Concept Drift Detection.

[BibT_eX]

[DOI]

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

Feature Selection for Concept Drift Detection.

[BibT_eX]

[DOI]

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

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Precise Change Point Detection using Spectral Drift Detection.

[BibT_eX]

[DOI]

CoRR, 2022

The SAME score: Improved cosine based bias score for word embeddings.

[BibT_eX]

[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.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XX, 2022

Feature Selection for Trustworthy Regression Using Higher Moments.

[BibT_eX]

[DOI]

Johannes Brinkrolf

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

Contrasting Explanation of Concept Drift.

[BibT_eX]

[DOI]

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

Federated learning vector quantization for dealing with drift between nodes.

[BibT_eX]

[DOI]

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

2021

Evaluating Metrics for Bias in Word Embeddings.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Fast Non-Parametric Conditional Density Estimation using Moment Trees.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

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

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Evaluating Robustness of Counterfactual Explanations.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Contrastive Explanations for Explaining Model Adaptations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Computational Intelligence, 2021

Concept Drift Segmentation via Kolmogorov-Trees.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information.

[BibT_eX]

[DOI]

Neurocomputing, 2020

Analysis of Drifting Features.

[BibT_eX]

[DOI]

Jonathan Jakob

CoRR, 2020

Counterfactual Explanations of Concept Drift.

[BibT_eX]

[DOI]

CoRR, 2020

DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction.

[BibT_eX]

[DOI]

Alexander Schulz

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD).

[BibT_eX]

[DOI]

André Artelt

Proceedings of the 37th International Conference on Machine Learning, 2020

Explaining Concept Drift by Mean of Direction.

[BibT_eX]

[DOI]

Johannes Kummert

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

2019

A probability theoretic approach to drifting data in continuous time domains.

[BibT_eX]

[DOI]

André Artelt

CoRR, 2019

DeepView: Visualizing the behavior of deep neural networks in a part of the data space.

[BibT_eX]

[DOI]

Alexander Schulz