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Found 7 papers, 2 papers with code
Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences
We introduce the $ARMOR_D$ methods as novel approaches to enhancing the adversarial robustness of deep learning models.
Function-space regularized Rényi divergences
We propose a new family of regularized R\'enyi divergences parametrized not only by the order $\alpha$ but also by a variational function space.
Structure-preserving GANs
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as a minmax problem based on the variational representation of a divergence between the unknown and the generated distributions.
A Variance Reduction Method for Neural-based Divergence Estimation
Unfortunately, the approximation of expectations that are inherent in variational formulas by statistical averages can be problematic due to high statistical variance, e. g., exponential for the Kullback-Leibler divergence and certain estimators.
$(f,Γ)$-Divergences: Interpolating between $f$-Divergences and Integral Probability Metrics
We develop a rigorous and general framework for constructing information-theoretic divergences that subsume both $f$-divergences and integral probability metrics (IPMs), such as the $1$-Wasserstein distance.
Variational Representations and Neural Network Estimation of Rényi Divergences
We further show that this R\'enyi variational formula holds over a range of function spaces; this leads to a formula for the optimizer under very weak assumptions and is also key in our development of a consistency theory for R\'enyi divergence estimators.
Optimizing Variational Representations of Divergences and Accelerating their Statistical Estimation
Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions.
