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Instead of struggling with fragile context tuning, treating divergent time series foundation model forecasts as complementary views in an identifiable latent space yields provably robust ensembles at test time without fine-tuning.
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.
