Importance Weighting and Variational Inference

27 August 2018

Papers citing "Importance Weighting and Variational Inference"

34 / 34 papers shown

Title
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders Yaniv Yacoby Weiwei Pan Finale Doshi-Velez DRL 34 0 0 13 Mar 2024
U-Statistics for Importance-Weighted Variational Inference Javier Burroni Kenta Takatsu Justin Domke Daniel Sheldon 18 1 0 27 Feb 2023
Explainability as statistical inference Hugo Senetaire Damien Garreau J. Frellsen Pierre-Alexandre Mattei FAtt 29 4 0 06 Dec 2022
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics Kamélia Daudel Joe Benton Yuyang Shi Arnaud Doucet DRL 21 8 0 12 Oct 2022
GFlowNets and variational inference Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 138 78 0 02 Oct 2022
Optimization of Annealed Importance Sampling Hyperparameters Shirin Goshtasbpour Fernando Perez-Cruz 37 1 0 27 Sep 2022
Compound Density Networks for Risk Prediction using Electronic Health Records Yuxi Liu S. Qin Zhenhao Zhang Wei Shao BDL 21 9 0 02 Aug 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 37 8 0 13 Jun 2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models Tomas Geffner Justin Domke 29 5 0 08 Mar 2022
Variational methods for simulation-based inference Manuel Glöckler Michael Deistler Jakob H. Macke 32 47 0 08 Mar 2022
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations Oskar Kviman Harald Melin Hazal Koptagel Victor Elvira J. Lagergren DRL 75 17 0 22 Feb 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling M. Jankowiak Du Phan BDL 35 13 0 22 Dec 2021
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization Haitao Liu Jiaqi Ding Xinyu Xie Xiaomo Jiang Yusong Zhao Xiaofang Wang BDL 34 14 0 20 Sep 2021
MCMC Variational Inference via Uncorrected Hamiltonian Annealing Tomas Geffner Justin Domke 33 34 0 08 Jul 2021
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence Ghassen Jerfel S. Wang Clara Fannjiang Katherine A. Heller Yi Ma Michael I. Jordan BDL 30 40 0 30 Jun 2021
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization Tomas Geffner Justin Domke 30 9 0 13 May 2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding Yangjun Ruan Karen Ullrich Daniel de Souza Severo James Townsend Ashish Khisti Arnaud Doucet Alireza Makhzani Chris J. Maddison 24 25 0 22 Feb 2021
Annealed Flow Transport Monte Carlo Michael Arbel A. G. Matthews Arnaud Doucet 45 70 0 15 Feb 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Evaluating Lossy Compression Rates of Deep Generative Models Sicong Huang Alireza Makhzani Yanshuai Cao Roger C. Grosse EGVM DRL 8 27 0 15 Aug 2020
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks Yaniv Yacoby Weiwei Pan Finale Doshi-Velez CML DRL 32 25 0 14 Jul 2020
Variational Inference with Continuously-Indexed Normalizing Flows Anthony L. Caterini R. Cornish Dino Sejdinovic Arnaud Doucet BDL 26 19 0 10 Jul 2020
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference Rob Brekelmans Vaden Masrani Frank Wood Greg Ver Steeg Aram Galstyan 22 16 0 01 Jul 2020
not-MIWAE: Deep Generative Modelling with Missing not at Random Data Niels Bruun Ipsen Pierre-Alexandre Mattei J. Frellsen DRL 14 54 0 23 Jun 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal Daniel Sheldon Justin Domke TPM BDL 19 38 0 18 Jun 2020
Bayesian Neural Network via Stochastic Gradient Descent Abhinav Sagar UQCV BDL 18 2 0 04 Jun 2020
Variational Autoencoder with Embedded Student- $t$ Mixture Model for Authorship Attribution Benedikt T. Boenninghoff Steffen Zeiler R. M. Nickel D. Kolossa BDL DRL 33 2 0 28 May 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 42 94 0 02 Mar 2020
Learning Discrete Distributions by Dequantization Emiel Hoogeboom Taco S. Cohen Jakub M. Tomczak DRL 34 31 0 30 Jan 2020
Particle Smoothing Variational Objectives A. Moretti Zizhao Wang Luhuan Wu Iddo Drori I. Pe’er 32 10 0 20 Sep 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke Daniel Sheldon 26 18 0 24 Jun 2019
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems Yibo Yang P. Perdikaris SyDa BDL AI4CE 29 55 0 15 Jan 2019
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Pierre-Alexandre Mattei J. Frellsen SyDa 25 45 0 06 Dec 2018
Variational Inference via $χ$ -Upper Bound Minimization Adji Bousso Dieng Dustin Tran Rajesh Ranganath John Paisley David M. Blei BDL 83 36 0 01 Nov 2016