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Sorbonne Université, Université Paris Cité
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Non-convex convergence rates for SGD in score-based generative models reveal how reweighting choices critically impact training efficiency and output quality.
Non-asymptotic error bounds reveal that biased proposals in SMC can be effectively managed, significantly improving the reliability of conditional diffusion sampling.
