Author

Anne Gagneux

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#machine learning Preprint Sep 2026

Principled MAP estimation for inverse problems: bridging the gap between convergence and performance

This paper designs an algorithm that combines denoisers at decreasing noise levels with a schedule tailored to ensure convergence, and proves that it surpasses convergent methods while competing with state-of-the-art empirical ones.

Alexandre Lagier, Valentine Tosel, Anne Gagneux et al. · 0 citations

#machine learning Preprint Sep 2026

Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching

This analysis identifies two main drivers for differences in source-- data signal-to-noise ratio, and the information bottleneck induced by the neural architecture, and introduces new spectral hybrid parameterizations that adapt across time and data covariance directions.

Ségolène Martin, Anne Gagneux, Quentin Bertrand et al. · 0 citations