Modern sequential monitoring problems often involve multiple metrics, where we monitor several data streams simultaneously and may act once the evidence is strong enough. Confidence sequences (CSs) are a natural tool for such continuous monitoring. However, for bounded vector means, existing multivariate CSs are either...
Fabian Denoodt, Sibylle Hess, J. Vanschoren et al.· 1 citation
Simulation-free training of latent Stochastic Differential Equations (SDEs) relies on a variational posterior process whose one-time marginals are tractable, typically Gaussian. Such marginals, however, do not determine the underlying dynamics: many processes share the same marginals while differing in their temporal s...
Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-range prediction, this formulation can suffer from severe error accumulation when used with shorter time steps and does not explicitly encode...
Maria Marchenko, Martin Andrae, F. Lindsten et al.· 0 citations
Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidence-based samplers. However, recent work has shown that such methods can defer unmasking high-entropy fork positions at which multiple plausible continuations exist. This...
S. Frkovic, Metod Jazbec, C. Naesseth· 0 citations
Based on this formulation of flow matching as variational inference, CatFlow is developed, a flow matching method for categorical data that is easy to implement, computationally efficient, and achieves strong results on graph generation tasks.
Floor Eijkelboom, G. Bartosh, C. Naesseth et al.· 0 citations
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