# Partial Inference in Structured Prediction

arXiv: https://arxiv.org/abs/2306.03949 · HTML: https://mlsanity.com/paper/2306.03949/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Exact Recovery in the Stochastic Block Model](https://mlsanity.com/paper/1405.3267/) (2014)
- [Scalable Semidefinite Programming](https://mlsanity.com/paper/1912.02949/) (2019)
- [On semidefinite relaxations for the block model](https://mlsanity.com/paper/1406.5647/) (2014)
- [Exact inference in structured prediction](https://mlsanity.com/paper/1906.00451/) (2019)
- [Inference in Sparse Graphs with Pairwise Measurements and Side Information](https://mlsanity.com/paper/1703.02728/) (2017)

## Built on by (most cited first, 1 of 1)

- [Provable Guarantees for Spectral Structured Prediction](https://mlsanity.com/paper/2609.18527/) (2026)

## Abstract (arXiv)

In this paper, we examine the problem of partial inference in the context of structured prediction. Using a generative model approach, we consider the task of maximizing a score function with unary and pairwise potentials in the space of labels on graphs. Employing a two-stage convex optimization algorithm for label recovery, we analyze the conditions under which a majority of the labels can be recovered. We introduce a novel perspective on the Karush-Kuhn-Tucker (KKT) conditions and primal and dual construction, and provide statistical and topological requirements for partial recovery with provable guarantees.