Conceptualizing Concept Drift

Roberts J (I), Hinder F, Vaquet V, Schulz A, Hammer B (2025)
In: ESANN 2025 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 561-566.

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

Vaquet V, Hinder F, Vaquet J, Lammers K, Quakernack L, Hammer B (2024)
In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

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

Vaquet V, Hinder F, Artelt A, Ashraf I, Strotherm J, Vaquet J, Brinkrolf J, Hammer B (2024)
In: Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX. Wand M, Malinovská K, Schmidhuber J, Tetko IV (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 155-170.

FairGLVQ: Fairness in Partition-Based Classification

Störck F, Hinder F, Brinkrolf J, Paaßen B, Vaquet V, Hammer B (2024)
In: Proceedings of the 15th International Workshop on Self-Organizing Maps (WSOM 2024). Villmann T, Kaden M, Geweniger T, Schleif F-M (Eds); Cham: Springer Nature Switzerland: 141-151.

One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift

Hinder F, Vaquet V, Hammer B (2024)
Frontiers in Artificial Intelligence 7.

Self-Supervised Learning from Incrementally Drifting Data Streams

Vaquet V, Vaquet J, Hinder F, Malialis K, Panayiotou C, Polycarpou M, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 431-436.

On the Fine Structure of Drifting Features

Hinder F, Vaquet V, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 63-68.

Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty

Brinkrolf J, Vaquet V, Hinder F, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 191-196.

A Remark on Concept Drift for Dependent Data

Hinder F, Vaquet V, Hammer B (2024)
In: Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Miliou I, Piatkowski N, Papapetrou P (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 77-89.

A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

Zanutto D, Michalopoulos C, Chatzistefanou G-A, Vamvakeridou-Lyroudia L, Tsiami L, Glynis K, Samartzis P, Hermes L, Hinder F, Vaquet J, Vaquet V, et al. (2024)
In: The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024). Basel Switzerland: MDPI: 60.

Feature-based analyses of concept drift

Hinder F, Vaquet V, Hammer B (2024)
Neurocomputing 600: 127968.

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

Hinder F, Vaquet V, Hammer B (2024)
Frontiers in Artificial Intelligence 7: 1330257.

Semantic Properties of Cosine Based Bias Scores for Word Embeddings

Schroeder S, Schulz A, Hinder F, Hammer B (2024)
In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 160-168.

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

Vaquet V, Hinder F, Hammer B (2024)
In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM. Volume 1. SCITEPRESS - Science and Technology Publications: 296-303.

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

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
In: Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Crémilleux B, Hess S, Nijssen S (Eds); Lecture Notes in Computer Science, 13876. Cham: Springer : 182-194.

On the Hardness and Necessity of Supervised Concept Drift Detection

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. De Marsico M, Sanniti di Baja G, Fred A (Eds); Setúbal: SCITEPRESS - Science and Technology Publications: 164-175.

Model-based explanations of concept drift

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
Neurocomputing: 126640.

Feature Selection for Concept Drift Detection

Hinder F, Hammer B (2023)
In: ESANN 2023 Proceedings. Verleysen M (Ed); .

Contrasting Explanations for Understanding and Regularizing Model Adaptations

Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2022)
Neural Processing Letters 55: 5273–5297.

Localization of Concept Drift: Identifying the Drifting Datapoints

Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022)
In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.

Federated learning vector quantization for dealing with drift between nodes

Vaquet V, Hinder F, Brinkrolf J, Menz P, Seiffert U, Hammer B (Accepted)
Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.

Localization of Concept Drift: Identifying the Drifting Datapoints

Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022) .

Suitability of Different Metric Choices for Concept Drift Detection

Hinder F, Vaquet V, Hammer B (2022)
In: Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Bouadi T, Fromont E, Hüllermeier E (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 157-170.

Contrastive Explanations for Explaining Model Adaptations

Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2021)
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer : 101-112.

Fast Non-Parametric Conditional Density Estimation using Moment Trees

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2021)
IEEE Computational Intelligence Magazine.

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

Hinder F, Brinkrolf J, Vaquet V, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 01-08.

Fast Non-Parametric Conditional Density Estimation using Moment Trees

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 1-7.

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

Vaquet V, Hinder F, Vaquet J, Brinkrolf J, Hammer B (2021)
IEEE Symposium Series on Computational Intelligence: 1-7.

Evaluating Robustness of Counterfactual Explanations

Artelt A, Vaquet V, Velioglu R, Hinder F, Brinkrolf J, Schilling M, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE: 01-09.

Concept Drift Segmentation via Kolmogorov Trees

Hinder F, Hammer B (Accepted)
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); .

Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

Hinder F, Artelt A, Hammer B (2020)
In: Proceedings of the 37th International Conference on Machine Learning.

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

Schulz A, Hinder F, Hammer B (2020)
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Neurocomputing.