# Manifold Structured Prediction

arXiv: https://arxiv.org/abs/1806.09908 · HTML: https://mlsanity.com/paper/1806.09908/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Differentially private Riemannian optimization](https://mlsanity.com/paper/2205.09494/) (2022)
- [Lifting Weak Supervision To Structured Prediction](https://mlsanity.com/paper/2211.13375/) (2022)
- [Geometry-Aware Adaptation for Pretrained Models](https://mlsanity.com/paper/2307.12226/) (2023)
- [A Structured Prediction Approach for Robot Imitation Learning](https://mlsanity.com/paper/2309.14829/) (2023)
- [Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models](https://mlsanity.com/paper/2102.12942/) (2021)

## Abstract (arXiv)

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study.