PhD Student
fhinder@techfak.uni-bielefeld.de +49 521 106-12376Room: CITEC 2-217
Fabian Hinder is a Ph.D. student in the Machine Learning group. He received his Master’s degree in pure mathematics from Bielefeld University in 2018. Since 2019, he has been a Ph.D. student at the Center for Cognitive Interaction Technology. His research interests cover learning in non-stationary environments, concept drift detection, statistical learning theory, explainable AI, and the foundations of machine learning.
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.
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.
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); .
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.
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.
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.
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) .
Fast Non-Parametric Conditional Density Estimation using Moment Trees
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2021)
IEEE Computational Intelligence Magazine.
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.
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.
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.
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); .
Schulz A, Hinder F, Hammer B (2020)
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.
Hinder F, Artelt A, Hammer B (2020)
In: Proceedings of the 37th International Conference on Machine Learning.
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Neurocomputing.