# Nonparametric plug-in classifier for multiclass classification of S.D.E. paths

arXiv: https://arxiv.org/abs/2212.10259 · HTML: https://mlsanity.com/paper/2212.10259/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Neural SDEs as Infinite-Dimensional GANs](https://mlsanity.com/paper/2102.03657/) (2021)
- [Depth for curve data and applications](https://mlsanity.com/paper/1901.00180/) (2019)

## Built on by (most cited first, 2 of 2)

- [Empirical risk minimization algorithm for multiclass classification of S.D.E. paths](https://mlsanity.com/paper/2503.14045/) (2025)
- [Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks](https://mlsanity.com/paper/2602.02791/) (2026)

## Abstract (arXiv)

We study the multiclass classification problem where the features come from the mixture of time-homogeneous diffusions. Specifically, the classes are discriminated by their drift functions while the diffusion coefficient is common to all classes and unknown. In this framework, we build a plug-in classifier which relies on nonparametric estimators of the drift and diffusion functions. We first establish the consistency of our classification procedure under mild assumptions and then provide rates of cnvergence under different set of assumptions. Finally, a numerical study supports our theoretical findings.