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Variational Boosting: Iteratively Refining Posterior Approximations

20 November 2016

Papers citing "Variational Boosting: Iteratively Refining Posterior Approximations"

48 / 48 papers shown

Title
Asymptotically exact variational flows via involutive MCMC kernels Zuheng Xu Trevor Campbell 28 0 0 02 Jun 2025
Stacking Variational Bayesian Monte Carlo Francesco Silvestrin Chengkun Li Luigi Acerbi BDL 102 0 0 07 Apr 2025
ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning Eric T. Nalisnick Christophe Ley Padhraic Smyth Thomas Hamelryck BDL 126 1 0 30 Oct 2024
Batch, match, and patch: low-rank approximations for score-based variational inference Chirag Modi Diana Cai Lawrence K. Saul BDL 99 3 0 29 Oct 2024
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
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
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation F. Igea Alice Cicirello 52 8 0 23 Feb 2022
Implicit copula variational inference M. Smith Rubén Loaiza-Maya 46 7 0 18 Nov 2021
Statistical Perspectives on Reliability of Artificial Intelligence Systems Yili Hong J. Lian Li Xu Jie Min Yueyao Wang Laura J. Freeman Xinwei Deng 68 33 0 09 Nov 2021
Is MC Dropout Bayesian? Loic Le Folgoc V. Baltatzis S. Desai A. Devaraj S. Ellis O. M. Manzanera A. Nair Huaqi Qiu Julia A. Schnabel Ben Glocker BDL OOD UQCV 98 41 0 08 Oct 2021
Variational Marginal Particle Filters Jinlin Lai Justin Domke Daniel Sheldon 95 9 0 30 Sep 2021
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence Ghassen Jerfel S. Wang Clara Fannjiang Katherine A. Heller Yi-An Ma Michael I. Jordan BDL 129 40 0 30 Jun 2021
Boosting Variational Inference With Locally Adaptive Step-Sizes Gideon Dresdner Saurav Shekhar Fabian Pedregosa Francesco Locatello Gunnar Rätsch 41 2 0 19 May 2021
The computational asymptotics of Gaussian variational inference and the Laplace approximation Zuheng Xu Trevor Campbell 105 8 0 13 Apr 2021
Boltzmann Tuning of Generative Models Victor Berger Michele Sebag 50 0 0 12 Apr 2021
Recursive Inference for Variational Autoencoders Minyoung Kim Vladimir Pavlovic DRL 50 13 0 17 Nov 2020
Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting B. Guha A. Bhattacharya D. Pati 78 2 0 19 Oct 2020
Learning from demonstration using products of experts: applications to manipulation and task prioritization Emmanuel Pignat João Silvério Sylvain Calinon 46 18 0 07 Oct 2020
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao Aki Vehtari Andrew Gelman 98 63 0 22 Jun 2020
Variational Bayesian Monte Carlo with Noisy Likelihoods Luigi Acerbi 102 42 0 15 Jun 2020
Gradient Boosted Normalizing Flows Robert Giaquinto A. Banerjee BDL DRL 16 1 0 27 Feb 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks J. Swiatkowski Kevin Roth Bastiaan S. Veeling Linh-Tam Tran Joshua V. Dillon Jasper Snoek Stephan Mandt Tim Salimans Rodolphe Jenatton Sebastian Nowozin BDL 74 47 0 07 Feb 2020
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages Edoardo Ponti Ivan Vulić Ryan Cotterell Marinela Parović Roi Reichart Anna Korhonen BDL 114 29 0 30 Jan 2020
Validated Variational Inference via Practical Posterior Error Bounds Jonathan H. Huggins Mikolaj Kasprzak Trevor Campbell Tamara Broderick 90 37 0 09 Oct 2019
BooVAE: Boosting Approach for Continual Learning of VAE Anna Kuzina Evgenii Egorov Evgeny Burnaev CLL 98 26 0 30 Aug 2019
Trust-Region Variational Inference with Gaussian Mixture Models Oleg Arenz Mingjun Zhong Gerhard Neumann 76 20 0 10 Jul 2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference Jiayu Yao Weiwei Pan S. Ghosh Finale Doshi-Velez UQCV BDL 193 114 0 24 Jun 2019
Universal Boosting Variational Inference Trevor Campbell Xinglong Li 59 32 0 04 Jun 2019
Variational Inference with Mixture Model Approximation: Robotic Applications Emmanuel Pignat Teguh Santoso Lembono Sylvain Calinon 19 2 0 23 May 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 71 8 0 23 May 2019
Conditionally structured variational Gaussian approximation with importance weights Linda S. L. Tan Aishwarya Bhaskaran David J. Nott 125 13 0 21 Apr 2019
High-dimensional copula variational approximation through transformation M. Smith Rubén Loaiza-Maya David J. Nott 79 33 0 16 Apr 2019
Manifold Optimization Assisted Gaussian Variational Approximation Bingxin Zhou Junbin Gao Minh-Ngoc Tran Richard Gerlach 67 6 0 11 Feb 2019
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient Aaron Mishkin Frederik Kunstner Didrik Nielsen Mark Schmidt Mohammad Emtiyaz Khan BDL UQCV 90 60 0 11 Nov 2018
Good Initializations of Variational Bayes for Deep Models Simone Rossi Pietro Michiardi Maurizio Filippone BDL 130 22 0 18 Oct 2018
Variational Bayesian Monte Carlo Luigi Acerbi BDL 68 66 0 12 Oct 2018
Variance reduction properties of the reparameterization trick Ming Xu M. Quiroz Robert Kohn Scott A. Sisson AAML 103 69 0 27 Sep 2018
Unbiased Implicit Variational Inference Michalis K. Titsias Francisco J. R. Ruiz BDL 134 57 0 06 Aug 2018
Boosting Black Box Variational Inference Francesco Locatello Gideon Dresdner Rajiv Khanna Isabel Valera Gunnar Rätsch 81 32 0 06 Jun 2018
Pathwise Derivatives for Multivariate Distributions M. Jankowiak Theofanis Karaletsos 121 11 0 05 Jun 2018
Semi-Implicit Variational Inference Mingzhang Yin Mingyuan Zhou BDL 89 128 0 28 May 2018
Gaussian variational approximation for high-dimensional state space models M. Quiroz David J. Nott Robert Kohn 112 40 0 24 Jan 2018
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference B. Thijssen L. Wessels 56 9 0 12 Dec 2017
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 233 698 0 15 Nov 2017
Reinterpreting Importance-Weighted Autoencoders Chris Cremer Q. Morris David Duvenaud BDL FAtt 135 94 0 10 Apr 2017
Boosted Generative Models Aditya Grover Stefano Ermon 82 51 0 27 Feb 2017
Boosting Variational Inference Fangjian Guo Xiangyu Wang Kai Fan Tamara Broderick David B. Dunson BDL 152 76 0 17 Nov 2016
Variational Particle Approximations A. Saeedi Tejas D. Kulkarni Vikash K. Mansinghka S. Gershman 186 60 0 24 Feb 2014