Binomial flows close the gap between continuous flow matching and discrete ordinal data by using binomial distributions to enable unified denoising, sampling, and exact likelihoods in diffusion models.
Denoising diffusion probabilistic models
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Novel algorithms for efficient learning of distributional regression trees optimized for CRPS and WIS losses via heaps, balanced trees, and Fenwick trees, with competitive performance and conformal prediction applications.
Lecture notes unify stochastic calculus, generator matching, and finite-sample Wasserstein guarantees for continuous-time Markovian generative models.
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Binomial flows: Denoising and flow matching for discrete ordinal data
Binomial flows close the gap between continuous flow matching and discrete ordinal data by using binomial distributions to enable unified denoising, sampling, and exact likelihoods in diffusion models.
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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts
Novel algorithms for efficient learning of distributional regression trees optimized for CRPS and WIS losses via heaps, balanced trees, and Fenwick trees, with competitive performance and conformal prediction applications.
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Statistical Analysis of Markovian Generative Modeling
Lecture notes unify stochastic calculus, generator matching, and finite-sample Wasserstein guarantees for continuous-time Markovian generative models.