Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Abstract
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains. However, principled DL methods for non-Euclidean random objects remain relatively scarce, despite the growing importance of modern biomedical outcomes taking the form of probability distributions, random graphs, functions, and other random objects in metric spaces. Motivated by a new generation of digital and clinical biomarkers, we propose a deep learning framework for regression with random objects in metric spaces. Our approach operates directly on the metric structure of the response space, avoiding restrictive manifold or Euclidean embedding assumptions imposed by many existing methods. At the same time, it retains modern statistical guarantees under the stated statistical assumptions. We establish statistical consistency and convergence rates for the proposed estimator, and characterize conditions under which it can mitigate the curse of dimensionality for hierarchically compositional regression functions. Extensive simulation studies demonstrate competitive predictive performance, with gains over several competing approaches in multiple Euclidean and non-Euclidean configurations. Finally, we demonstrate the practical utility of the proposed framework in two digital health case studies with distribution-valued outcomes in Wasserstein spaces, using high-resolution accelerometry and continuous glucose monitoring data. Embedding-based deep-learning methods may be less suitable when no informative low-dimensional representation exists or when the learned embedding does not adequately preserve the intrinsic geometry of the response space.
Related
- Metric Subtraction for Non-Euclidean Random Objects with Applications to Residual Analysis
- Intermediate Singular Values of Random Matrices under Second-Moment and Anti-Concentration Assumptions
- Marginal Coordinate Test for Fréchet Regression with Random Objects
- Exponential Smoothing for Time Series of Random Objects
- Simultaneous Inference for Centre-Outward Rankings of Random Objects
BibTeX
@misc{matabuena2026deep,
title = {{Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions}},
author = {Marcos Matabuena and Xiaokai Luo and Oscar Hernan Madrid Padilla},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31328},
eprint = {2610.31328},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31328},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
AU - Marcos Matabuena
AU - Xiaokai Luo
AU - Oscar Hernan Madrid Padilla
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.31328
UR - https://arxiv.org/abs/2610.31328
AB - Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains. However, principled DL methods for non-Euclidean random objects remain relatively scarce, despite the growing importance of modern biomedical outcomes taking the form of probability distributions, random graphs, functions, and other random objects in metric spaces. Motivated by a new generation of digital and clinical biomarkers, we propose a deep learning framework for regression with random objects in metric spaces. Our approach operates directly on the metric structure of the response space, avoiding restrictive manifold or Euclidean embedding assumptions imposed by many existing methods. At the same time, it retains modern statistical guarantees under the stated statistical assumptions. We establish statistical consistency and convergence rates for the proposed estimator, and characterize conditions under which it can mitigate the curse of dimensionality for hierarchically compositional regression functions. Extensive simulation studies demonstrate competitive predictive performance, with gains over several competing approaches in multiple Euclidean and non-Euclidean configurations. Finally, we demonstrate the practical utility of the proposed framework in two digital health case studies with distribution-valued outcomes in Wasserstein spaces, using high-resolution accelerometry and continuous glucose monitoring data. Embedding-based deep-learning methods may be less suitable when no informative low-dimensional representation exists or when the learned embedding does not adequately preserve the intrinsic geometry of the response space.
ER -