Author

C. Naesseth

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Preprint Aug 2026

On the Tightness and Computational Tractability of Higher-Dimensional Confidence Sequences

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. · 0 citations

#machine learning Preprint Sep 2026

Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models

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

UvA-DARE (Digital Academic Repository) Variational Flow Matching for Graph Generation

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