26 citations · 36 across the 4 of their papers we have counts for
4 papers
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
Marc Velay, Bich-Liên Doan, Arpad Rimmel +2
Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implie…
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel +1
Anomaly detection is vital in many domains, such as finance, healthcare, and cybersecurity. In this paper, we propose a novel deep anomaly detection method for tabular data that le…
TracInAD: Measuring Influence for Anomaly Detection
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel +2
As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular…
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
Benoît Choffin, Fabrice Popineau, Yolaine Bourda +1
Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to impr…