Model-based explanations of concept drift
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
Neurocomputing: 126640.
Johannes Brinkrolf
postdoc
jbrinkro@techfak.uni-bielefeld.de +49 521 106-12135Room: CITEC 2-227
Johannes Brinkrolf received his Master’s degree from Bielefeld University in 2016. From April 2016 to September 2016, he was a research assistant at the South Westphalia University of Applied Science. From October 2016 to July 2023, he was a Ph.D. student in the Machine Learning group at the Center for Cognitive Interaction Technology at Bielefeld University. In July 2023, he defended his thesis dealing with applications of learning vector quantization models. His research interests cover differential privacy, federated and distributed learning, foundations of machine learning, and prototype-based learning models. Since January 2023, he has been working as a member of the IT service of the Faculty of Technology.
Publications:
Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity
Brinkrolf J (2023)
Bielefeld: Universität Bielefeld.
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.
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.
Explaining Reject Options of Learning Vector Quantization Classifiers
Artelt A, Brinkrolf J, Visser R, Hammer B (2022)
In: Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications: 249-261.
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) .
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.
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.
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.
Federated Learning Vector Quantization
Brinkrolf J, Hammer B (Accepted)
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); .
Sparse Metric Learning in Prototype-based Classification
Brinkrolf J, Hammer B (2020)
In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
Differential privacy for learning vector quantization
Brinkrolf J, Göpfert C, Hammer B (2019)
Neurocomputing 342: 125-136.
Time integration and reject options for probabilistic output of pairwise LVQ
Brinkrolf J, Hammer B (2019)
Neural Computing and Applications.
Differential private relevance learning
Brinkrolf J, Berger K, Hammer B (2018)
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
Interpretable Machine Learning with Reject Option
Brinkrolf J, Hammer B (2018)
at - Automatisierungstechnik 66(4): 283-290.
Probabilistic extension and reject options for pairwise LVQ
Brinkrolf J, Hammer B (2017)
In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
Efficient Kernelization of Discriminative Dimensionality Reduction
Schulz A, Brinkrolf J, Hammer B (2017)
Neurocomputing 268(SI): 34-41.
Differential Privacy for Learning Vector Quantization
Brinkrolf J, Berger K, Hammer B (2017)
In: New Challenges in Neural Computation.
Virtual optimisation for improved production planning
Brinkrolf J, Mittag T, Joppen R, Dr\ A, Pietsch K-H, Hammer B (2016)
In: New Challenges in Neural Computation.