# A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings

arXiv: https://arxiv.org/abs/2012.03780 · HTML: https://mlsanity.com/paper/2012.03780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

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

- [On Certified Generalization in Structured Prediction](https://mlsanity.com/paper/2306.09112/) (2023)

## Abstract (arXiv)

Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent. Recent theoretical advances in structured prediction have focused on obtaining fast rates convergence guarantees, especially in the Implicit Loss Embedding (ILE) framework. PAC-Bayes has gained interest recently for its capacity of producing tight risk bounds for predictor distributions. This work proposes a novel PAC-Bayes perspective on the ILE Structured prediction framework. We present two generalization bounds, on the risk and excess risk, which yield insights into the behavior of ILE predictors. Two learning algorithms are derived from these bounds. The algorithms are implemented and their behavior analyzed, with source code available at urlhttps://github.com/theophilec/PAC-Bayes-ILE-Structured-Prediction.