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Andrea Dittadi

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On Closed-Form Couplings
Tobias Höppe
Stefan Bauer
Qiang Liu
Andrea Dittadi
Kirill Neklyudov
Few-step generative modelling is an open challenge for flow models. Rectified flows tackle it by distilling a pre-trained “teacher” into… (voir plus) a few-step “student”, using strong noise–data couplings supplied by the teacher. For a finite dataset and a Gaussian probability path, the probability-flow vector field induced by the empirical distribution is available in closed form, which would allow us to skip training a teacher model. Surprisingly, these couplings turn out to be poor teachers and significantly reduce the performance of the student. We analyse this phenomenon empirically and theoretically, arguing that it stems from intrinsic ambiguity in the induced couplings caused by the strong sensitivity of terminal states to small initialisation perturbations. Under symmetry assumptions, we further prove that the closed-form probability-flow vector field preserves dataset symmetries and induces invariant Voronoi partitions.
2026-03-02
DeLTa @ International Conference on Learning Representations (poster)
openreview.net
openreview.net
Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction
Vincent Pauline
Tobias Höppe
Kirill Neklyudov
Alexander Tong
Stefan Bauer
Andrea Dittadi
2025-12-03
ArXiv (prépublication)
doi.org
arxiv.org
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
Alessandro Palma
Till Richter
Hanyi Zhang
Manuel Lubetzki
Alexander Tong
Andrea Dittadi
Fabian J. Theis
Generative modeling of single-cell RNA-seq data is crucial for tasks like trajectory inference, batch effect removal, and simulation of real… (voir plus)istic cellular data. However, recent deep generative models simulating synthetic single cells from noise operate on pre-processed continuous gene expression approximations, overlooking the discrete nature of single-cell data, which limits their effectiveness and hinders the incorporation of robust noise models. Additionally, aspects like controllable multi-modal and multi-label generation of cellular data remain underexplored. This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data. CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics while tackling relevant generative tasks such as rare cell type augmentation and batch correction. We also introduce a novel framework for compositional data generation using Flow Matching. By showcasing CFGen on a diverse set of biological datasets and settings, we provide evidence of its value to the fields of computational biology and deep generative models.
2025-04-22
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net