5 papers
A Fair Classifier Embracing Triplet Collapse
A. Martzloff, N. Posocco, Q. Ferré
In this paper, we study the behaviour of the triplet loss and show that it can be exploited to limit the biases created and perpetuated by machine learning models. Our fair classif…
Calibrate to Interpret
Gregory Scafarto, Nicolas Posocco, Antoine Bonnefoy
Trustworthy machine learning is driving a large number of ML community works in order to improve ML acceptance and adoption. The main aspect of trustworthy machine learning are the…
Estimating Expected Calibration Errors
Nicolas Posocco, Antoine Bonnefoy
Uncertainty in probabilistic classifiers predictions is a key concern when models are used to support human decision making, in broader probabilistic pipelines or when sensitive au…
DAEMA: Denoising Autoencoder with Mask Attention
Simon Tihon, Muhammad Usama Javaid, Damien Fourure +2
Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing data imputation has bec…
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol
Damien Fourure, Muhammad Usama Javaid, Nicolas Posocco +1
Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets.…