A Large Margin Framework For Learning Structured Prediction Models

By Editorial Team · September 29, 2026 · 4 min read

A Large Margin Framework For Learning Structured Prediction Models Information Guide

  1. About to A Large Margin Framework For Learning Structured Prediction Models
  2. Important Facts
  3. Developments
  4. Deep Dive
  5. Final Thoughts

About to A Large Margin Framework For Learning Structured Prediction Models

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Important Facts

Details Structured Prediction with Indirect Supervision News
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Developments

Full Max-margin training and inference on structured models for information extrac... Update
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Tractable Learning of Structured Prediction Models
Tractable Learning of Structured Prediction Models
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
On Structured Prediction Theory with Calibrated Convex Surrogate Losses (NIPS 2017)
On Structured Prediction Theory with Calibrated Convex Surrogate Losses (NIPS 2017)
Practical Learning Algorithms for Structured Prediction
Practical Learning Algorithms for Structured Prediction
A Historical View of Large Margin Optimization Methods
A Historical View of Large Margin Optimization Methods
Neural Network Models for Structured Prediction June 23, 2017
Neural Network Models for Structured Prediction June 23, 2017
Efficient Inference and Learning for Structured Models
Efficient Inference and Learning for Structured Models
IR20.2 Large margin classification
IR20.2 Large margin classification
Joint(Structured) Prediction and Learning, part 1
Joint(Structured) Prediction and Learning, part 1
KDD 2023 - Robustness Certification Structured Prediction with General Inputs Safe Region Modeling
KDD 2023 - Robustness Certification Structured Prediction with General Inputs Safe Region Modeling
Large Margin Generative Models
Large Margin Generative Models

Deep Dive

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Last Updated: September 29, 2026

Final Thoughts

Scott Yang: A Theoretical Framework for Structured Prediction using Factor Graph Complexity Update
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Summary

We present a novel statistical estimation Google Tech Talks June, 2 2008 ABSTRACT Feature-based Talk at the NIPS Workshop on Multi-class and Multi-label With the recent advances in machine A teaser video on the NIPS 2017 paper "On Starting from the design of robust Hopfield works in the early 1990s to SVMs to Sensors acquire an increasing amount of diverse information posing two challenges. Firstly, how can we efficiently deal with such ... Shao Huaqing, Shanghai Jiao Tong University.

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