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Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations

7 June 2019

Wu Lin

Mohammad Emtiyaz Khan

Mark Schmidt

BDL

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Papers citing "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"

49 / 49 papers shown

Title
Variational Inference with Mixtures of Isotropic Gaussians Marguerite Petit-Talamon Marc Lambert Anna Korba BDL 41 0 0 16 Jun 2025
Variational Formulation of the Particle Flow Particle Filter Yinzhuang Yi Jorge Cortés Nikolay Atanasov 71 0 0 06 May 2025
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 171 0 0 10 Feb 2025
Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient Shaoqi Wang Chunjie Yang Siwei Lou 40 1 0 23 Sep 2024
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities José A. Carrillo Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Dongyi Wei AI4CE 91 4 0 22 Jul 2024
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 74 7 0 25 Jun 2024
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians Tom Huix Anna Korba Alain Durmus Eric Moulines 89 9 0 06 Jun 2024
Understanding Stochastic Natural Gradient Variational Inference Kaiwen Wu Jacob R. Gardner BDL 87 2 0 04 Jun 2024
Outlier-robust Kalman Filtering through Generalised Bayes Gerardo Duran-Martín Matias Altamirano Alexander Y. Shestopaloff Leandro Sánchez-Betancourt Jeremias Knoblauch Matt Jones F. Briol Kevin P. Murphy 138 11 0 09 May 2024
Variational Learning is Effective for Large Deep Networks Yuesong Shen Nico Daheim Bai Cong Peter Nickl Gian Maria Marconi ... Rio Yokota Iryna Gurevych Daniel Cremers Mohammad Emtiyaz Khan Thomas Möllenhoff 91 31 0 27 Feb 2024
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard Turner Alireza Makhzani ODL 161 13 0 05 Feb 2024
Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko Kyurae Kim W. Kim Jacob R. Gardner BDL 72 4 0 19 Jan 2024
Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling Fei Ding David E. Jones Shiyuan He Xiao-Li Meng OT 51 0 0 01 Jan 2024
Joint State Estimation and Noise Identification Based on Variational Optimization Hua Lan Shijie Zhao Jinjie Hu Zengfu Wang Jing-Zhi Fu 41 2 0 15 Dec 2023
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen Tetsuya Sakurai Hiroshi Mamitsuka 139 2 0 25 Oct 2023
Optimising Distributions with Natural Gradient Surrogates Jonathan So Richard Turner 34 1 0 18 Oct 2023
Sampling via Gradient Flows in the Space of Probability Measures Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 75 15 0 05 Oct 2023
Variational Inference with Gaussian Score Matching Chirag Modi C. Margossian Yuling Yao Robert Mansel Gower David M. Blei Lawrence K. Saul 83 13 0 15 Jul 2023
Variational Gaussian Process Diffusion Processes Prakhar Verma Vincent Adam Arno Solin DiffM 133 6 0 03 Jun 2023
On the Convergence of Black-Box Variational Inference Kyurae Kim Jisu Oh Kaiwen Wu Yi-An Ma Jacob R. Gardner BDL 94 17 0 24 May 2023
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent Tianle Liu Promit Ghosal Krishnakumar Balasubramanian Natesh S. Pillai 111 11 0 23 May 2023
Variational Bayes Made Easy Mohammad Emtiyaz Khan BDL 51 1 0 27 Apr 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 68 29 0 10 Apr 2023
The Lie-Group Bayesian Learning Rule E. M. Kıral Thomas Möllenhoff Mohammad Emtiyaz Khan BDL 52 3 0 08 Mar 2023
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models Weiben Zhang M. Smith Worapree Maneesoonthorn Rubén Loaiza-Maya 31 1 0 27 Feb 2023
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 76 19 0 21 Feb 2023
Structured variational approximations with skew normal decomposable graphical models Roberto Salomone Xue Yu David J. Nott Robert Kohn 77 2 0 07 Feb 2023
BALANCE: Bayesian Linear Attribution for Root Cause Localization Chaoyu Chen Hang Yu Zhichao Lei Jianguo Li Shaokang Ren Tingkai Zhang Si-Yu Hu Jianchao Wang Wenhui Shi 69 7 0 31 Jan 2023
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms Thomas Guilmeau Émilie Chouzenoux Victor Elvira 86 3 0 09 Nov 2022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models Oleg Arenz Philipp Dahlinger Zihan Ye Michael Volpp Gerhard Neumann 126 17 0 23 Sep 2022
Variational inference via Wasserstein gradient flows Marc Lambert Sinho Chewi Francis R. Bach Silvère Bonnabel Philippe Rigollet BDL DRL 101 77 0 31 May 2022
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition Randy Ardywibowo Shahin Boluki Zhangyang Wang Bobak J. Mortazavi Shuai Huang Xiaoning Qian 43 2 0 31 Mar 2022
Natural Gradient Variational Inference with Gaussian Mixture Models F. Mahdisoltani BDL 43 1 0 15 Nov 2021
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation Linda S. L. Tan 108 13 0 01 Sep 2021
Variational inference for cutting feedback in misspecified models Xue Yu David J. Nott M. Smith 73 14 0 25 Aug 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 162 83 0 09 Jul 2021
Flexible Variational Bayes based on a Copula of a Mixture David Gunawan Robert Kohn David J. Nott 107 12 0 28 Jun 2021
Stein Latent Optimization for Generative Adversarial Networks Uiwon Hwang Heeseung Kim Dahuin Jung Hyemi Jang Hyungyu Lee Sungroh Yoon GAN 119 2 0 09 Jun 2021
Boosting Variational Inference With Locally Adaptive Step-Sizes Gideon Dresdner Saurav Shekhar Fabian Pedregosa Francesco Locatello Gunnar Rätsch 43 2 0 19 May 2021
A practical tutorial on Variational Bayes Minh-Ngoc Tran Trong-Nghia Nguyen Viet-Hung Dao BDL 79 38 0 01 Mar 2021
Challenges and Opportunities in High-dimensional Variational Inference Akash Kumar Dhaka Alejandro Catalina Manushi K. V. Welandawe Michael Riis Andersen Jonathan H. Huggins Aki Vehtari 50 42 0 01 Mar 2021
Tractable structured natural gradient descent using local parameterizations Wu Lin Frank Nielsen Mohammad Emtiyaz Khan Mark Schmidt 146 30 0 15 Feb 2021
Meta-strategy for Learning Tuning Parameters with Guarantees Dimitri Meunier Pierre Alquier 102 8 0 04 Feb 2021
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts Sehwan Kim Qifan Song F. Liang BDL 34 12 0 20 Sep 2020
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks Alexander Shekhovtsov Dmitry Molchanov MQ 79 16 0 11 Jun 2020
The role of exchangeability in causal inference O. Saarela D. Stephens E. Moodie 120 7 0 02 Jun 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule Wu Lin Mark Schmidt Mohammad Emtiyaz Khan BDL 181 36 0 24 Feb 2020
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures Wu Lin Mohammad Emtiyaz Khan Mark Schmidt 121 31 0 29 Oct 2019
Posterior inference unchained with EL_2O U. Seljak Byeonghee Yu 62 10 0 14 Jan 2019