Battle of Water Demand Forecasting

Alvisi S, Franchini M, Marsili V, Mazzoni F, Salomons E, Housh M, Abokifa A, Arsova K, Ayyash F, Bae H, Barreira R, et al. (2025)
Journal of Water Resources Planning and Management 151(10).

Compression-based $k$NN for Class Incremental Continual Learning

Vaquet V, Vaquet J, Hinder F, Hammer B (2025)
In: ESANN 2025 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 45-50.

Adversarial Attacks for Drift Detection

Hinder F, Vaquet V, Hammer B (2025)
In: ESANN 2025 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 555-560.

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.