# Localized Structured Prediction

@article{Ciliberto2019LocalizedSP, title={Localized Structured Prediction}, author={Carlo Ciliberto and Francis R. Bach and Alessandro Rudi}, journal={ArXiv}, year={2019}, volume={abs/1806.02402} }

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…

## 24 Citations

### A Structured Prediction Approach for Conditional Meta-Learning

- Computer ScienceArXiv
- 2020

This work proposes task-adaptive structured meta- learning (TASML), a principled estimator that weighs meta-training data conditioned on the target task to design tailored meta-learning objectives and introduces algorithmic improvements to tackle key computational limitations of existing methods.

### Manifold Structured Prediction

- Computer ScienceNeurIPS
- 2018

A structured prediction approach to manifold-valued regression is studied and a class of problems for which the considered approach is statistically consistent is characterized and how geometric optimization can be used to compute the corresponding estimator is studied.

### Fine-grained Generalization Analysis of Structured Output Prediction

- Computer ScienceIJCAI
- 2021

This paper significantly improves the state of the art by developing novel high-probability bounds with a logarithmic dependency on d and leverages the lens of algorithmic stability to develop generalization bounds in expectation without any dependency ond.

### A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings

- Computer Science, MathematicsJ. Mach. Learn. Res.
- 2020

A large class of loss functions is identified and study that implicitly defines a suitable geometry on the problem that is the key to develop an algorithmic framework amenable to a sharp statistical analysis and yielding efficient computations.

### Structured Prediction for Conditional Meta-Learning

- Computer ScienceNeurIPS
- 2020

TASML is derived, a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks and improves the performance of existing meta-learning models, and outperforms the state-of-the-art on benchmark datasets.

### Deep Structured Prediction with Nonlinear Output Transformations

- Computer ScienceNeurIPS
- 2018

A novel model is developed which generalizes existing approaches, such as structured prediction energy networks, and a formulation is discussed which maintains applicability of existing inference techniques.

### 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.

### PABI: A UNIFIED PAC-BAYESIAN INFORMATIVENESS MEASURE FOR INCIDENTAL SUPERVISION SIGNALS

- Computer Science
- 2020

PABI is proposed, a unified informativeness measure motivated by PAC-Bayesian theory, characterizing the reduction in uncertainty that indirect, weak signals provide, and its use in quantifying various types of incidental signals including partial labels, noisy labels, constraints, cross-domain signals, and combinations of these.

### Foreshadowing the Benefits of Incidental Supervision

- Computer ScienceArXiv
- 2020

A unified PAC-Bayesian Informativeness measure (PABI) is proposed, characterizing the reduction in uncertainty that incidental supervision signals provide and demonstrating PABI's use in quantifying various types of incidental signals.

### Foreseeing the Benefits of Incidental Supervision

- Computer ScienceEMNLP
- 2021

A unified PAC-Bayesian motivated informativeness measure, PABI, is proposed that characterizes the uncertainty reduction provided by incidental supervision signals, and its effectiveness is demonstrated by quantifying the value added by various types of incidental signals to sequence tagging tasks.

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