Stream graphs for fraud detection
Stream graphs applied to fraud detection.
Hugo Thimonier PhD thesis defense
The thesis is in the Machine Learning domain and its title is “Advancing Anomaly Detection in Tabular Data: A Case-Study on Credit Card Fraud Identification”, under the supervision of Bich-Liên DOAN, Fabrice POPINEAU and Arpad RIMMEL.
The defense will happen on Monday, September 30th at 14:00 in the room 435 of the LISN laboratory. The defense will be in English. For those who cannot attend in person, there will be a visio-conference whose link will be found at https://popineau.pages.centralesupelec.fr/soutenance-hugo-thimonier/ .
ICML 2024
Hugo Thimonier is at ICML2024 to present Beyond Individual Input for Deep Anomaly Detection on Tabular Data
2024 Workshop on Anomaly Detection
Workshop on Anomaly Detection
We’re pleased to invite you to take part in the symposium on anomaly detection to be held on February 29 at CentraleSupélec. It’s also an opportunity for your teams’ researchers/doctoral students to present their research work on the subject, either in the form of a 10-minute oral presentation, or a poster. Please send your intention to participate to chaire-lusis@centralesupelec.fr by 15th February 2024.
Subject: Symposium on anomaly detection
NeurIPS 2023
Hugo Thimonier and Bich-Liên Doan are at NeurIPS2023 for the Table Representation Learning Workshop
Graph-Based and Time-Series Hybrid Modeling for Fraud Detection
This project develops a hybrid fraud detection model by combining graph-based learning and time-series analysis to capture complex transaction relationships, aiming to enhance fraud prediction accuracy while minimizing false positives in large-scale payment datasets.
JDSE 2022
The Junior Conference on DataScience and Engeneering 2022 (JDSE) took place on september 15-16, on the Polytechnique campus (Palaiseau).
Hugo Thimonier has the opportunity to present “TracInAD: Measuring Influence for Anomaly Detection” to the audience.
Marc Velay won the best poster contest with his poster about “Robustness Analysis of Deep RL for Portfolio Selection”.
Hopular applied to Fraud Detection
Hopular applied to Fraud Detection.
Explainability applied to fraud detection
Constructing probabilistic rule list.