# Localized Structured Prediction

arXiv: https://arxiv.org/abs/1806.02402 · HTML: https://mlsanity.com/paper/1806.02402/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

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

- [Approximation and Learning with Deep Convolutional Models: a Kernel Perspective](https://mlsanity.com/paper/2102.10032/) (2021)
- [The Exact Sample Complexity Gain from Invariances for Kernel Regression](https://mlsanity.com/paper/2303.14269/) (2023)
- [On the Sample Complexity of Learning under Invariance and Geometric Stability](https://mlsanity.com/paper/2106.07148/) (2021)
- [Fine-grained Generalization Analysis of Structured Output Prediction](https://mlsanity.com/paper/2106.00115/) (2021)
- [On Certified Generalization in Structured Prediction](https://mlsanity.com/paper/2306.09112/) (2023)

## Abstract (arXiv)

Key to structured prediction is exploiting the problem structure to simplify the learning process. A major challenge arises when data exhibit a local structure (e.g., are made by "parts") that can be leveraged to better approximate the relation between (parts of) the input and (parts of) the output. Recent literature on signal processing, and in particular computer vision, has shown that capturing these aspects is indeed essential to achieve state-of-the-art performance. While such algorithms are typically derived on a case-by-case basis, in this work we propose the first theoretical framework to deal with part-based data from a general perspective. We derive a novel approach to deal with these problems and study its generalization properties within the setting of statistical learning theory. Our analysis is novel in that it explicitly quantifies the benefits of leveraging the part-based structure of the problem with respect to the learning rates of the proposed estimator.