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Found 7 papers, 2 papers with code
A Statistical Learning Take on the Concordance Index for Survival Analysis
The introduction of machine learning (ML) techniques to the field of survival analysis has increased the flexibility of modeling approaches, and ML based models have become state-of-the-art.
On the Consistency of Max-Margin Losses
The resulting loss is also a generalization of the binary support vector machine and it is consistent under milder conditions on the discrete loss.
Consistent Structured Prediction with Max-Min Margin Markov Networks
Max-margin methods for binary classification such as the support vector machine (SVM) have been extended to the structured prediction setting under the name of max-margin Markov networks ($M^3N$), or more generally structural SVMs.
Structured and Localized Image Restoration
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.
A General Theory for Structured Prediction with Smooth Convex Surrogates
In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e. g. logistic regression).
Sharp Analysis of Learning with Discrete Losses
The problem of devising learning strategies for discrete losses (e. g., multilabeling, ranking) is currently addressed with methods and theoretical analyses ad-hoc for each loss.
Divide and Conquer Networks
Moreover, thanks to the dynamic aspect of our architecture, we can incorporate the computational complexity as a regularization term that can be optimized by backpropagation.
