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 predict...
| Authors | Nic Fishman, Gokul Gowri, Paolo L. B. Fischer et al. |
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| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 25 September 2026 |
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