On the Convergence of Black-Box Variational Inference

On the Convergence of Black-Box Variational Inference

24 May 2023

Papers citing "On the Convergence of Black-Box Variational Inference"

10 / 10 papers shown

Title
You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference Dominic B. Dayta TPM BDL 21 0 0 05 Jun 2024
Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim Joohwan Ko Yian Ma Jacob R. Gardner 48 0 0 03 Jun 2024
Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko Kyurae Kim W. Kim Jacob R. Gardner BDL 25 1 0 19 Jan 2024
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space Yiheng Jiang Sinho Chewi Aram-Alexandre Pooladian 25 7 0 05 Dec 2023
Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be Frederik Kunstner Jacques Chen J. Lavington Mark W. Schmidt 40 66 0 27 Apr 2023
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box Ryan Giordano Martin Ingram Tamara Broderick 56 12 0 11 Apr 2023
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms Thomas Guilmeau Émilie Chouzenoux Victor Elvira 19 3 0 09 Nov 2022
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint Y. Freund Yi-An Ma Tong Zhang 18 16 0 05 Oct 2021
Markovian Score Climbing: Variational Inference with KL(p\|\|q) C. A. Naesseth Fredrik Lindsten David M. Blei 108 54 0 23 Mar 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 119 1,194 0 16 Aug 2016