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Preprint Sep 2026

A Unified Dual Method for Matching Problems

Matching problems are ubiquitous in data science as they enable the alignment of structured objects and distributions. While existing solvers are often tailored to specific matching formulations, we unify a broad class of such problems within a common mathematical and optimization framework based on duality theory. The...

Guillaume Houry, SU FerdinandGenans, Lpsm et al. · 0 citations
#data science Jul 2026

Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

This analysis quantifies the role of the reweighting factor in the score approximation error, providing theoretical guidance for weighting choices used in practice, and develops a Neural Tangent Kernel analysis tailored to diffusion training with stochastic gradients.

Stanislas Strasman, Sobihan Surendran, S. Le Corff · 0 citations

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