wasserstein gradient flow

A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization JMLR 2026 Particle Dynamics for Latent-Variable Energy-Based Models UAI 2026 Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space ICLR 2025 DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows AISTATS 2025 Importance Corrected Neural JKO Sampling ICML 2025 Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality (Extended Abstract) COLT 2025 Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry ICLR 2025 Repulsive Latent Score Distillation for Solving Inverse Problems ICLR 2025 Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds JMLR 2025 Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference AISTATS 2025 WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction ICML 2025 Global Convergence in Training Large-Scale Transformers NeurIPS 2024 Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior AAAI 2024 Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning ICML 2024 Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow AISTATS 2024 Open problem: Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games COLT 2024 Particle Semi-Implicit Variational Inference NeurIPS 2024 Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective NeurIPS 2024 Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport ICML 2024 Symmetries in Overparametrized Neural Networks: A Mean Field View NeurIPS 2024 Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape ICML 2024 A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods NeurIPS 2023 Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023 MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows ICML 2023 Normalizing flow neural networks by JKO scheme NeurIPS 2023