AI RESEARCH

Faithful Embeddings of Irregular and Asynchronous Data for Online Log-NCDEs

arXiv CS.LG

ArXi:2605.30213v1 Announce Type: new Continuous-time models are a natural choice for irregular and asynchronous data. A central design choice is how to embed discrete observations into continuous time. Interpolation- and imputation-based embeddings reconstruct a continuous observation path, making the model sensitive to the choice of reconstruction. We show that this reconstruction step is unnecessary; under mild conditions, compact-set universality on the model input space transfers to the data space whenever the embedding from data to input is continuous and injective.