Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)

no code implementations • 18 Mar 2024 • Paul Novello, Joseba Dalmau, Léo Andeol

Based on the work of (Bates et al., 2022), we define new conformal AUROC and conformal FRP@TPR95 metrics, which are corrections that provide probabilistic conservativeness guarantees on the variability of these metrics.

Anomaly Detection Conformal Prediction +2

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

1 code implementation • 9 Jun 2021 • Léo Andeol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon

However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain.