# Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time

@article{Ghoshal2018LearningMP, title={Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time}, author={Asish Ghoshal and Jean Honorio}, journal={ArXiv}, year={2018}, volume={abs/1805.08196} }

MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and facilitate predictions that are robust to random noise. In this paper, we propose a provably polynomial time randomized algorithm for learning the parameters of perturbed MAP predictors. Our approach is based on minimizing a novel Rademacher-based generalization bound on the expected loss of a perturbed MAP predictor…

## 4 Citations

### Minimax bounds for structured prediction

- Computer ScienceArXiv
- 2019

This work provides minimax bounds for a class of factor-graph inference models for structured prediction, which characterize the necessary sample complexity for any conceivable algorithm to achieve learning offactor-graph predictors.

### Towards Sharper Generalization Bounds for Structured Prediction

- Computer ScienceNeurIPS
- 2021

This paper investigates the generalization performance of structured prediction learning and obtains state-of-the-art generalization bounds from three different perspectives: Lipschitz continuity, smoothness, and space capacity condition.

### Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning

- Computer Science2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- 2021

A novel transfer learning method based on deep Gaussian processes (DGPs) that achieves the best inference latencies of convolutions while accelerating the optimization process significantly, compared with previous arts.

### Minimax Bounds for Structured Prediction Based on Factor Graphs

- Computer ScienceAISTATS
- 2020

This work provides minimax lower bounds for a class of general factor-graph inference models in the context of structured prediction, and characterize the necessary sample complexity for any conceivable algorithm to achieve learning of generalfactor-graph predictors.

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