Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence

30 June 2021

Papers citing "Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence"

31 / 31 papers shown

Title
Improving the evaluation of samplers on multi-modal targets Louis Grenioux Maxence Noble Marylou Gabrié 146 0 0 11 Apr 2025
Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective Jia-Jie Zhu 68 1 0 31 Oct 2024
Learned Reference-based Diffusion Sampling for multi-modal distributions Maxence Noble Louis Grenioux Marylou Gabrié Alain Durmus DiffM 31 2 0 25 Oct 2024
A theoretical perspective on mode collapse in variational inference Roman Soletskyi Marylou Gabrié Bruno Loureiro DRL 37 2 0 17 Oct 2024
SoftCVI: Contrastive variational inference with self-generated soft labels Daniel Ward Mark Beaumont Matteo Fasiolo BDL 53 0 0 22 Jul 2024
Torchtree: flexible phylogenetic model development and inference using PyTorch Mathieu Fourment Matthew Macaulay Christiaan J. Swanepoel Xiang Ji M. Suchard Frederick A Matsen IV BDL 29 0 0 26 Jun 2024
Doubly Adaptive Importance Sampling W. van den Boom Andrea Cremaschi Alexandre H. Thiery 26 0 0 29 Apr 2024
Stable Training of Normalizing Flows for High-dimensional Variational Inference Daniel Andrade BDL TPM 43 1 0 26 Feb 2024
Forward $χ^2$ Divergence Based Variational Importance Sampling Chengrui Li Yule Wang Weihan Li Anqi Wu BDL 25 2 0 04 Nov 2023
Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows Mingxuan Yi Song Liu DRL 33 8 0 31 Oct 2023
Stochastic automatic differentiation for Monte Carlo processes Guilherme Catumba A. Ramos B. Zaldívar 25 7 0 28 Jul 2023
Balanced Training of Energy-Based Models with Adaptive Flow Sampling Louis Grenioux Eric Moulines Marylou Gabrié 26 2 0 01 Jun 2023
On Sampling with Approximate Transport Maps Louis Grenioux Alain Durmus Eric Moulines Marylou Gabrié OT 27 15 0 09 Feb 2023
Structured variational approximations with skew normal decomposable graphical models Roberto Salomone Xue Yu David J. Nott Robert Kohn 24 2 0 07 Feb 2023
Learning to Generate All Feasible Actions Mirco Theile Daniele Bernardini Raphael Trumpp C. Piazza Marco Caccamo Alberto L. Sangiovanni-Vincentelli 29 2 0 26 Jan 2023
Sliced Wasserstein Variational Inference Mingxuan Yi Song Liu 17 19 0 26 Jul 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 32 8 0 13 Jun 2022
Variational methods for simulation-based inference Manuel Glöckler Michael Deistler Jakob H. Macke 30 46 0 08 Mar 2022
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport Liyi Zhang David M. Blei C. A. Naesseth 30 6 0 03 Feb 2022
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 32 7 0 02 Jan 2022
Variational Bayes for high-dimensional proportional hazards models with applications within gene expression M. Komodromos E. Aboagye Marina Evangelou Sarah Filippi Kolyan Ray 14 9 0 19 Dec 2021
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences Alan Chan Hugo Silva Sungsu Lim Tadashi Kozuno A. R. Mahmood Martha White 25 29 0 17 Jul 2021
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models Lily H. Zhang Mark Goldstein Rajesh Ranganath OODD 143 103 0 14 Jul 2021
Flexible Variational Bayes based on a Copula of a Mixture David Gunawan Robert Kohn David J. Nott 8 12 0 28 Jun 2021
Nested Variational Inference Heiko Zimmermann Hao Wu Babak Esmaeili Jan-Willem van de Meent BDL 27 20 0 21 Jun 2021
Mixture weights optimisation for Alpha-Divergence Variational Inference Kamélia Daudel Randal Douc 18 8 0 09 Jun 2021
Greedy Bayesian Posterior Approximation with Deep Ensembles A. Tiulpin Matthew B. Blaschko UQCV FedML 36 4 0 29 May 2021
Monotonic Alpha-divergence Minimisation for Variational Inference Kamélia Daudel Randal Douc François Roueff 17 8 0 09 Mar 2021
Asymptotic Analysis of Conditioned Stochastic Gradient Descent Rémi Leluc Franccois Portier 14 2 0 04 Jun 2020
Distilling Importance Sampling for Likelihood Free Inference D. Prangle Cecilia Viscardi 19 3 0 08 Oct 2019
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,267 0 09 Jun 2012