Automatic Differentiation Variational Inference

2 March 2016

Papers citing "Automatic Differentiation Variational Inference"

43 / 93 papers shown

Title
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal Daniel Sheldon Justin Domke TPM BDL 8 38 0 18 Jun 2020
The role of exchangeability in causal inference O. Saarela D. Stephens E. Moodie 36 5 0 02 Jun 2020
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 16 6 0 28 Mar 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 11 0 0 02 Mar 2020
Joint Distributions for TensorFlow Probability Dan Piponi Dave Moore Joshua V. Dillon GP 27 16 0 22 Jan 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression V. Lalchand C. Rasmussen GP 33 51 0 31 Dec 2019
Gradient-based Adaptive Markov Chain Monte Carlo Michalis K. Titsias P. Dellaportas BDL 39 22 0 04 Nov 2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support Yuanshuo Zhou Hongseok Yang Yee Whye Teh Tom Rainforth TPM 29 19 0 29 Oct 2019
Uncertainty Quantification with Generative Models Vanessa Böhm F. Lanusse U. Seljak 30 25 0 22 Oct 2019
Inference of a mesoscopic population model from population spike trains M. Slawski A. Longtin E. Ben-David 27 12 0 03 Oct 2019
Generating Data using Monte Carlo Dropout Kristian Miok Dong Nguyen Doan D. Zaharie Marko Robnik-Šikonja SyDa 16 13 0 12 Sep 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 45 397 0 25 Jun 2019
Analyzing the Role of Model Uncertainty for Electronic Health Records Michael W. Dusenberry Dustin Tran Edward Choi Jonas Kemp Jeremy Nixon Ghassen Jerfel Katherine A. Heller Andrew M. Dai 10 117 0 10 Jun 2019
Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability Shaowu Pan Karthik Duraisamy 23 136 0 09 Jun 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 32 8 0 23 May 2019
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records Linying Zhang Yixin Wang A. Ostropolets J. J. Mulgrave David M. Blei G. Hripcsak BDL CML 15 1 0 03 Apr 2019
Encoding prior knowledge in the structure of the likelihood Jakob Knollmüller T. Ensslin 36 11 0 11 Dec 2018
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 24 55 0 27 Nov 2018
Effect Handling for Composable Program Transformations in Edward2 Dave Moore Maria I. Gorinova 6 15 0 15 Nov 2018
Composing Modeling and Inference Operations with Probabilistic Program Combinators Eli Sennesh Adam Scibior Hao Wu Jan-Willem van de Meent TPM 13 1 0 14 Nov 2018
Pymc-learn: Practical Probabilistic Machine Learning in Python Daniel Emaasit GP 11 4 0 31 Oct 2018
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model Daniel Andrade Akiko Takeda Kenji Fukumizu 29 4 0 15 Jun 2018
Pathwise Derivatives Beyond the Reparameterization Trick M. Jankowiak F. Obermeyer 30 110 0 05 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 19 30 0 01 Jun 2018
Wasserstein Variational Inference L. Ambrogioni Umut Güçlü Yağmur Güçlütürk Max Hinne E. Maris Marcel van Gerven BDL DRL 19 42 0 29 May 2018
Semi-Implicit Variational Inference Mingzhang Yin Mingyuan Zhou BDL 35 121 0 28 May 2018
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers Marcel Hirt P. Dellaportas BDL 20 10 0 23 May 2018
The Blessings of Multiple Causes Yixin Wang David M. Blei AI4CE CML 24 284 0 17 May 2018
Variational Inference In Pachinko Allocation Machines Akash Srivastava Charles Sutton 18 6 0 21 Apr 2018
The Description Length of Deep Learning Models Léonard Blier Yann Ollivier 32 95 0 20 Feb 2018
Gaussian variational approximation for high-dimensional state space models M. Quiroz David J. Nott Robert Kohn 24 40 0 24 Jan 2018
How well does your sampler really work? Ryan D. Turner Brady Neal 27 4 0 16 Dec 2017
Variational Deep Q Network Yunhao Tang A. Kucukelbir BDL 38 10 0 30 Nov 2017
TensorFlow Distributions Joshua V. Dillon I. Langmore Dustin Tran E. Brevdo Srinivas Vasudevan David A. Moore Brian Patton Alexander A. Alemi Matt Hoffman Rif A. Saurous GP 46 346 0 28 Nov 2017
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 22 210 0 25 May 2017
Frequentist Consistency of Variational Bayes Yixin Wang David M. Blei BDL 28 204 0 09 May 2017
Differentially Private Variational Inference for Non-conjugate Models Joonas Jälkö O. Dikmen Antti Honkela FedML 21 48 0 27 Oct 2016
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms C. A. Naesseth Francisco J. R. Ruiz Scott W. Linderman David M. Blei BDL 25 107 0 18 Oct 2016
Gaussian variational approximation with sparse precision matrices Linda S. L. Tan David J. Nott 30 76 0 18 May 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 38 4,710 0 04 Jan 2016
The Variational Gaussian Process Dustin Tran Rajesh Ranganath David M. Blei BDL 21 184 0 20 Nov 2015
Hierarchical Variational Models Rajesh Ranganath Dustin Tran David M. Blei DRL VLM 17 335 0 07 Nov 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 54 2,750 0 20 Feb 2015