Author

Aurélien Renault

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Preprint Aug 2026

End-to-end Early Classification of Time Series in Non-Stationary Environments

DQeND is introduced, a unified architecture that jointly learns representation, classification, and triggering decisions, while remaining directly comparable to state-of-the-art separable baselines, and indicates that end-to-end learning can offer improved adaptation capabilities for ECTS in dynamic environments.

Aurélien Renault, A. Bondu, Antoine Cornuéjols et al. · 0 citations

#machine learning Preprint Feb 2025

Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL

This paper introduces Alert, a Deep-RL framework that learns trigger functions from any state representation, and proposes Alert+, a simple yet effective variant that consistently outperforms traditional methods in balancing accuracy and delay within an imbalanced misclassification and exponential delay cost setting.

Aurélien Renault, A. Bondu, Antoine Cornuéjols et al. · 2 citations