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Found 10 papers, 3 papers with code
Nonlinear denoising score matching for enhanced learning of structured distributions
We present a novel method for training score-based generative models which uses nonlinear noising dynamics to improve learning of structured distributions.
Learning heavy-tailed distributions with Wasserstein-proximal-regularized $α$-divergences
In this paper, we propose Wasserstein proximals of $\alpha$-divergences as suitable objective functionals for learning heavy-tailed distributions in a stable manner.
Statistical Guarantees of Group-Invariant GANs
Group-invariant generative adversarial networks (GANs) are a type of GANs in which the generators and discriminators are hardwired with group symmetries.
Sample Complexity of Probability Divergences under Group Symmetry
We rigorously quantify the improvement in the sample complexity of variational divergence estimations for group-invariant distributions.
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate new samples.
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.
Model Uncertainty and Correctability for Directed Graphical Models
Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence.
$(f,Γ)$-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.
