Cover Image

Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kim, Kyurae, Fu, Qiang, Ma, Yi-An, Gardner, Jacob R., Campbell, Trevor
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Optimization and Control Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
by: Kim, Kyurae, et al.
Published: (2025)

Demystifying SGD with Doubly Stochastic Gradients
by: Kim, Kyurae, et al.
Published: (2024)

On the Convergence of Black-Box Variational Inference
by: Kim, Kyurae, et al.
Published: (2023)

Stochastic Approximation with Biased MCMC for Expectation Maximization
by: Gruffaz, Samuel, et al.
Published: (2024)

Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026)

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent
by: Surjanovic, Nikola, et al.
Published: (2025)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
by: Bonet, Clément, et al.
Published: (2024)

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)

AutoGD: Automatic Learning Rate Selection for Gradient Descent
by: Surjanovic, Nikola, et al.
Published: (2025)

Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
by: Yamamoto, Naoya, et al.
Published: (2025)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
by: Keane, Ronan
Published: (2024)

Stochastic Gradients under Nuisances
by: Yu, Facheng, et al.
Published: (2025)

Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
by: Ren, Yinuo, et al.
Published: (2023)

Stochastic Gradient Methods with Preconditioned Updates
by: Sadiev, Abdurakhmon, et al.
Published: (2022)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)

Bilevel Learning with Inexact Stochastic Gradients
by: Salehi, Mohammad Sadegh, et al.
Published: (2024)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
by: Ding, Kuangyu, et al.
Published: (2023)

Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
by: Yang, Yufeng, et al.
Published: (2024)

Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)

Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective
by: Zhu, Jia-Jie
Published: (2024)

Stochastic Gradient Langevin Dynamics with Variance Reduction
by: Huang, Zhishen, et al.
Published: (2021)

Derivatives of Stochastic Gradient Descent in parametric optimization
by: Iutzeler, Franck, et al.
Published: (2024)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024)

Gradient-Variation Online Learning under Generalized Smoothness
by: Xie, Yan-Feng, et al.
Published: (2024)

Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)

Parameter-free Clipped Gradient Descent Meets Polyak
by: Takezawa, Yuki, et al.
Published: (2024)

On the Role of Batch Size in Stochastic Conditional Gradient Methods
by: Islamov, Rustem, et al.
Published: (2026)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Riemannian Stochastic Gradient Method for Nested Composition Optimization
by: Zhang, Dewei, et al.
Published: (2022)

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
by: Köhne, Frederik, et al.
Published: (2023)

Functional Central Limit Theorem for Stochastic Gradient Descent
by: Flamand, Kessang, et al.
Published: (2026)

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)

A Homogenization Approach for Gradient-Dominated Stochastic Optimization
by: Tan, Jiyuan, et al.
Published: (2023)

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
by: Altekrüger, Fabian, et al.
Published: (2023)

Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
by: Vansover-Hager, Shira, et al.
Published: (2025)