Cover Image

On the Convergence of Black-Box Variational Inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kim, Kyurae, Oh, Jisu, Wu, Kaiwen, Ma, Yi-An, Gardner, Jacob R.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Signal Processing 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)

Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
by: Kim, Kyurae, et al.
Published: (2026)

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
by: Kim, Kyurae, et al.
Published: (2023)

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

Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoisers
by: Pritchard, Henry, et al.
Published: (2025)

A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval
by: Zheng, Zhong, et al.
Published: (2023)

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

An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
by: Balzano, Laura, et al.
Published: (2025)

The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
by: Xu, Xingyu, et al.
Published: (2023)

Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
by: Ma, Ziye, et al.
Published: (2024)

Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks
by: Zhang, Aoran, et al.
Published: (2026)

Provably Scalable Black-Box Variational Inference with Structured Variational Families
by: Ko, Joohwan, et al.
Published: (2024)

Dictionary Learning under Symmetries via Group Representations
by: Ghosh, Subhroshekhar, et al.
Published: (2023)

Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
by: de Vazelhes, William, et al.
Published: (2023)

Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
by: Noah, Yoav, et al.
Published: (2023)

Optimized Gradient Tracking for Decentralized Online Learning
by: Sharma, Shivangi Dubey, et al.
Published: (2023)

Accelerated First-Order Optimization under Nonlinear Constraints
by: Muehlebach, Michael, et al.
Published: (2023)

Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
by: Suwandi, Richard Cornelius, et al.
Published: (2023)

Efficient algorithms for the Hadamard decomposition
by: Wertz, Samuel, et al.
Published: (2025)

Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data
by: Seraghiti, Giovanni, et al.
Published: (2026)

Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
by: Awari, Atharva, et al.
Published: (2025)

Robust Low-rank Tensor Train Recovery
by: Qin, Zhen, et al.
Published: (2024)

Near-Optimal Solutions of Constrained Learning Problems
by: Elenter, Juan, et al.
Published: (2024)

Federated Learning Using Three-Operator ADMM
by: Kant, Shashi, et al.
Published: (2022)

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
by: Wei, Yudong, et al.
Published: (2025)

Simple Alternating Minimization Provably Solves Complete Dictionary Learning
by: Liang, Geyu, et al.
Published: (2022)

On the Crucial Role of Initialization for Matrix Factorization
by: Li, Bingcong, et al.
Published: (2024)

Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition
by: Qin, Zhen, et al.
Published: (2024)

ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring
by: Ansari, Rayan, et al.
Published: (2024)

Localized Distributional Robustness in Submodular Multi-Task Subset Selection
by: Kaya, Ege C., et al.
Published: (2024)

A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm
by: Leong, Oscar, et al.
Published: (2025)

Locality Regularized Reconstruction: Structured Sparsity and Delaunay Triangulations
by: Mueller, Marshall, et al.
Published: (2024)

Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery
by: Qin, Zhen, et al.
Published: (2024)

A Scalable Factorization Approach for High-Order Structured Tensor Recovery
by: Qin, Zhen, et al.
Published: (2025)

Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
by: Taha, Feras Al, et al.
Published: (2026)

Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
by: Hertrich, Johannes, et al.
Published: (2020)

Outlier-robust Autocovariance Least Square Estimation via Iteratively Reweighted Least Square
by: Li, Jiahong, et al.
Published: (2026)

Checking the Sufficiently Scattered Condition using a Global Non-Convex Optimization Software
by: Gillis, Nicolas, et al.
Published: (2024)

Exponentially Weighted Moving Models
by: Luxenberg, Eric, et al.
Published: (2024)

Piecewise Linearity of Min-Norm Solution Map of a Nonconvexly Regularized Convex Sparse Model
by: Zhang, Yi, et al.
Published: (2023)