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Found 9 papers, 0 papers with code
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
Optimal Budgeted Rejection Sampling for Generative Models
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models.
Ranked #14 on
Image Generation
on CelebA 64x64
Training Normalizing Flows with the Precision-Recall Divergence
Generative models can have distinct mode of failures like mode dropping and low quality samples, which cannot be captured by a single scalar metric.
Robust empirical risk minimization via Newton's method
A new variant of Newton's method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing literature on robust mean estimation for multivariate data.
The Many Faces of Adversarial Risk
Adversarial risk quantifies the performance of classifiers on adversarially perturbed data.
Adversarial Risk via Optimal Transport and Optimal Couplings
We show that the optimal adversarial risk for binary classification with 0-1 loss is determined by an optimal transport cost between the probability distributions of the two classes.
Active Learning with Importance Sampling
We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data.
Graph-Based Ascent Algorithms for Function Maximization
We also provide simulations showing the relative convergence rates of our algorithms in comparison to an unbiased random walk, as a function of the smoothness of the graph function.
On Consistency of Compressive Spectral Clustering
Spectral clustering is one of the most popular methods for community detection in graphs.
