Distribution-Conditioned Transport
Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require models that can generalize to source and target distributions unseen during training. We introduce distribution-conditioned transport (DCT), a framework that conditions transport maps on learned embeddings of source and target distributions, enabling generalization to unseen distribution pairs. DCT also allows semi-supervised learning for distributional forecasting problems: because it learns from arbitrary distribution pairs, it can leverage distributions observed at only one condition to improve transport prediction. DCT is agnostic to the underlying transport mechanism, supporting models ranging from flow matching to distributional divergence-based models (e.g. Wasserstein, MMD). We demonstrate the practical performance benefits of DCT on synthetic benchmarks and four applications in biology: batch effect transfer in single-cell genomics, perturbation prediction from mass cytometry data, learning clonal transcriptional dynamics in hematopoiesis, and modeling T-cell receptor sequence evolution.
69aa4852c4f6edddcc6c863c
Nic Fishman
69aa4852c4f6edddcc6c863d
Gokul Gowri
69aa4852c4f6edddcc6c863e
Paolo L. B. Fischer
69aa4852c4f6edddcc6c863f
Marinka Zitnik
69aa4852c4f6edddcc6c8640
Omar Abudayyeh
69aa4852c4f6edddcc6c8641
Jonathan Gootenberg
Distribution-Conditioned Transport
Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require models that can generalize to source and target distributions unseen during training. We introduce distribution-conditioned transport (DCT), a framework that conditions transport maps on learned embeddings of source and target distributions, enabling generalization to unseen distribution pairs. DCT also allows semi-supervised learning for distributional forecasting problems: because it learns from arbitrary distribution pairs, it can leverage distributions observed at only one condition to improve transport prediction. DCT is agnostic to the underlying transport mechanism, supporting models ranging from flow matching to distributional divergence-based models (e.g. Wasserstein, MMD). We demonstrate the practical performance benefits of DCT on synthetic benchmarks and four applications in biology: batch effect transfer in single-cell genomics, perturbation prediction from mass cytometry data, learning clonal transcriptional dynamics in hematopoiesis, and modeling T-cell receptor sequence evolution.
Distribution-conditioned transport framework enables generalization to unseen distribution pairs and supports semi-supervised learning for scientific applications.
distribution-conditioned transport transport maps learned embeddings generalization semi-supervised learning flow matching Wasserstein MMD single-cell genomics mass cytometry hematopoiesis T-cell receptor sequence evolution