Realistic Benchmarks for Fair Stream Learning

Lammers K, Vaquet V, Vaquet J, Hammer B (2026)
In: Neural Information Processing. 32nd International Conference, ICONIP 2025, Okinawa, Japan, November 20–24, 2025, Proceedings, Part III. Taniguchi T, Leung CSA, Kozuno T, Yoshimoto J, Mahmud M, Doborjeh M, Doya K (Eds); Communications in Computer and Information Science. Singapore: Springer Nature Singapore: 169-184.

Large Language Models Do Not Simulate Human Psychology

Schroeder S, Morgenroth T, Kuhl U, Vaquet V, Paaßen B (2025)
arXiv.

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.

Continuous Fair SMOTE – Fairness-Aware Stream Learning from Imbalanced Data

Lammers K, Vaquet V, Hammer B (2025)
In: Artificial Neural Networks and Machine Learning – ICANN 2025. 34th International Conference on Artificial Neural Networks, Kaunas, Lithuania, September 9–12, 2025, Proceedings, Part I. Senn W, Sanguineti M, Saudargiene A, Tetko IV, Villa AEP, Jirsa V, Bengio Y (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 336-348.

Realistic Benchmarks for Fair Stream Learning

Lammers K, Vaquet V, Vaquet J, Hammer B (Submitted) .

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.

Feature-based analyses of concept drift

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

LSTM Autoencoder Model to Identify Electric Vehicles in Grouped Smart Meter Data

Quakernack L, Engelmann T, Haubrock J, Vaquet V (2024)
In: Comference Book 2024 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE, ISGT EUROPE. IEEE PES Innovative Smart Grid Technologies Conference Europe. New York: Institute of Electrical and Electronics Engineers (IEEE).

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.

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.

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.

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

Vaquet V, Brinkrolf J, Hammer B (Accepted) .

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.

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.

Model-based explanations of concept drift

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

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.

Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data

Vaquet V, Menz P, Seiffert U, Hammer B (2022)
Neurocomputing.

Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks

Vaquet V, Artelt A, Brinkrolf J, Hammer B (2022)
Presented at the 31st International Conference on Artificial Neural Networks, Bristol.

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.

Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data

Vaquet V, Menz P, Seiffert U, Hammer B (2021)
In: ESANN 2021 proceedings. Verleysen M (Ed); Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 47-52.

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.

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

Vaquet V, Hammer B (2020)
In: Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Farkaš I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12397. Cham: Springer: 850-862.

Continuous online user authentication based on keystroke dynamics

Artelt A, Jakob J, Vaquet V (2019)
Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany.