Corpus ID: 53232072

Distributionally Robust Graphical Models

@inproceedings{Fathony2018DistributionallyRG,
  title={Distributionally Robust Graphical Models},
  author={Rizal Fathony and Ashkan Rezaei and Mohammad Ali Bashiri and Xinhua Zhang and Brian D. Ziebart},
  booktitle={NeurIPS},
  year={2018}
}
In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks. Conditional random fields (CRFs) are Fisher consistent, but they do not permit integration of customized loss metrics into their learning process. Large-margin models, such as structured… Expand
Consistent Structured Prediction with Max-Min Margin Markov Networks
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
Distributionally Robust Learning
Correlation Robust Influence Maximization
Distributionally Robust Bayesian Quadrature Optimization
A Distributionally Robust Boosting Algorithm
Virtual Adversarial Training for Semi-supervised Verification Tasks
Distributionally Robust Optimization: A Review
...
1
2
...

References

SHOWING 1-10 OF 49 REFERENCES
ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-label Classification
Learning structured prediction models: a large margin approach
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Large Margin Methods for Structured and Interdependent Output Variables
Adversarial Prediction Games for Multivariate Losses
An Introduction to Conditional Random Fields
...
1
2
3
4
5
...