PhD Student
vvaquet@techfak.uni-bielefeld.de +49 521 106-12137Room: CITEC 2-226
Valerie Vaquet is a Ph.D. student in the Machine Learning group at Bielefeld University, Germany. She received her Master’s degree in Intelligent Systems from Bielefeld University in 2020. Her research interests cover learning in non-stationary environments, sensor calibration and fault detection in hyper-spectral data and water distribution networks. Next to her research, she is interested in science communication and participated in many science slams. In 2019 she scored second in the German FameLab competition.
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.
Model-based explanations of concept drift
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
Neurocomputing: 126640.
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.
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.
Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data
Vaquet V, Menz P, Seiffert U, Hammer B (2022)
Neurocomputing.
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.
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.
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.
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.