Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
Trajectory Inference (TI) seeks to recover latent dynamical processes from snapshot data, where only independent samples from time-indexed marginals are observed. In applications such as single-cell genomics, destructive measurements make path-space laws non-identifiable from finitely many marginals, leaving held-out marginal prediction as the dominant but limited evaluation protocol. We introduce a general framework for estimating the Kullback-Leibler divergence (KL) divergence between probability measures on function space, yielding a tractable, data-driven estimator that is scalable to realistic snapshot datasets. We validate the accuracy of our estimator on a benchmark suite, where th...
| Authors | Chao Wang, Luca Nepote, Giulio Franzese et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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