wasserstein gradient flow

Particle-based Variational Inference with Generalized Wasserstein Gradient Flow NeurIPS 2023 Self-Consistent Velocity Matching of Probability Flows NeurIPS 2023 Accurate Quantization of Measures via Interacting Particle-based Optimization ICML 2022 DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework IJCAI 2022 Improved analysis for a proximal algorithm for sampling COLT 2022 Improving Task-free Continual Learning by Distributionally Robust Memory Evolution ICML 2022 Online Learning to Transport via the Minimal Selection Principle COLT 2022 Provably convergent quasistatic dynamics for mean-field two-player zero-sum games ICLR 2022 Variational inference via Wasserstein gradient flows NeurIPS 2022 Dataset Dynamics via Gradient Flows in Probability Space ICML 2021 Kernel Stein Discrepancy Descent ICML 2021 Large-Scale Wasserstein Gradient Flows NeurIPS 2021 Modified Frank Wolfe in Probability Space NeurIPS 2021 Repulsive Deep Ensembles are Bayesian NeurIPS 2021 Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data ICML 2021 A Dynamical Central Limit Theorem for Shallow Neural Networks NeurIPS 2020 A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models AISTATS 2020 Stochastically Dominant Distributional Reinforcement Learning ICML 2020 The Wasserstein Proximal Gradient Algorithm NeurIPS 2020 Variance Reduction in Stochastic Particle-Optimization Sampling ICML 2020 Maximum Mean Discrepancy Gradient Flow NeurIPS 2019 Scalable Thompson Sampling via Optimal Transport AISTATS 2019 Understanding and Accelerating Particle-Based Variational Inference ICML 2019 Langevin Monte Carlo and JKO splitting COLT 2018 On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport NeurIPS 2018