
Proximal Mapping for Deep Regularization
Underpinning the success of deep learning is effective regularizations t...
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Convex Representation Learning for Generalized Invariance in SemiInnerProduct Space
Invariance (defined in a general sense) has been one of the most effecti...
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Generalised Lipschitz Regularisation Equals Distributional Robustness
The problem of adversarial examples has highlighted the need for a theor...
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Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multicl...
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Distributionally Robust Graphical Models
In many structured prediction problems, complex relationships between va...
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ExpConcavity of Proper Composite Losses
The goal of online prediction with expert advice is to find a decision s...
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DSMLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Scaling multinomial logistic regression to datasets with very large numb...
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Generalized Conditional Gradient for Sparse Estimation
Structured sparsity is an important modeling tool that expands the appli...
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Convex Relaxations of Bregman Divergence Clustering
Although many convex relaxations of clustering have been proposed in the...
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Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
We demonstrate that almost all nonparametric dimensionality reduction m...
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Smoothing Multivariate Performance Measures
A Support Vector Method for multivariate performance measures was recent...
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Lower Bounds for BMRM and Faster Rates for Training SVMs
Regularized risk minimization with the binary hinge loss and its variant...
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Xinhua Zhang
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