wasserstein distance

Nested-Wasserstein Self-Imitation Learning for Sequence Generation AISTATS 2020 On Relativistic f-Divergences ICML 2020 Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets NeurIPS 2020 Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NeurIPS 2020 Private k-Means Clustering with Stability Assumptions AISTATS 2020 Projection Robust Wasserstein Distance and Riemannian Optimization NeurIPS 2020 Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality NeurIPS 2020 Quantile Propagation for Wasserstein-Approximate Gaussian Processes NeurIPS 2020 Quantitative Propagation of Chaos for SGD in Wide Neural Networks NeurIPS 2020 Ratio Trace Formulation of Wasserstein Discriminant Analysis NeurIPS 2020 Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport AISTATS 2020 Regularizing activations in neural networks via distribution matching with the Wasserstein metric ICLR 2020 Representation Learning via Adversarially-Contrastive Optimal Transport ICML 2020 Robust Document Distance with Wasserstein-Fisher-Rao metric ACML 2020 Robust Federated Learning: The Case of Affine Distribution Shifts NeurIPS 2020 Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation NeurIPS 2020 Scalable Nearest Neighbor Search for Optimal Transport ICML 2020 Severity-Aware Semantic Segmentation With Reinforced Wasserstein Training CVPR 2020 Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold AAAI 2020 Statistical and Topological Properties of Sliced Probability Divergences NeurIPS 2020 Stochastic Optimization for Regularized Wasserstein Estimators ICML 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Stronger and Faster Wasserstein Adversarial Attacks ICML 2020 SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NeurIPS 2020 Understanding Gradient Clipping in Private SGD: A Geometric Perspective NeurIPS 2020