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Volume 118: Symposium on Advances in Approximate Bayesian Inference, 08 December 2019,

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Editors: Cheng Zhang, Francisco Ruiz, Thang Bui, Adji Bousso Dieng, Dawen Liang

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Rapid Model Comparison by Amortizing Across Models

Lily H. Zhang,  Michael C. Hughes ; PMLR 118:1-11

Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders

Yaniv Yacoby,  Weiwei Pan,  Finale Doshi-Velez ; PMLR 118:1-17

AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms

Kai Xu,  Hong Ge,  Will Tebbutt,  Mohamed Tarek,  Martin Trapp,  Zoubin Ghahramani ; PMLR 118:1-10

Variational Gaussian Process Models without Matrix Inverses

Mark van der Wilk,  ST John,  Artem Artemev,  James Hensman ; PMLR 118:1-9

Information in Infinite Ensembles of Infinitely-Wide Neural Networks

Ravid Shwartz-Ziv,  Alexander A Alemi ; PMLR 118:1-17

Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models

Rishit Sheth,  Roni Khardon ; PMLR 118:1-18

MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

Yura Perov,  Logan Graham,  Kostis Gourgoulias,  Jonathan Richens,  Ciaran Lee,  Adam Baker,  Saurabh Johri ; PMLR 118:1-36

The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels

Michael Pearce ; PMLR 118:1-12

Neural Permutation Processes

Ari Pakman,  Yueqi Wang,  Liam Paninski ; PMLR 118:1-7

Sinkhorn Permutation Variational Marginal Inference

Gonzalo Mena,  Erdem Varol,  Amin Nejatbakhsh,  Eviatar Yemini,  Liam Paninski ; PMLR 118:1-9

Improving Sequential Latent Variable Models with Autoregressive Flows

Joseph Marino,  Lei Chen,  Jiawei He,  Stephan Mandt ; PMLR 118:1-16

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals

Chao Ma,  Sebastian Tschiatschek,  Yingzhen Li,  Richard Turner,  Jose Miguel Hernandez-Lobato,  Cheng Zhang ; PMLR 118:1-8

Scalable Gradients and Variational Inference for Stochastic Differential Equations

Xuechen Li,  Ting-Kam Leonard Wong,  Ricky T. Q. Chen,  David K. Duvenaud ; PMLR 118:1-28

Approximate Inference for Fully Bayesian Gaussian Process Regression

Vidhi Lalchand,  Carl Edward Rasmussen ; PMLR 118:1-12

Normalizing Constant Estimation with Gaussianized Bridge Sampling

He Jia,  Uros Seljak ; PMLR 118:1-14

Variational Bayesian Methods for Stochastically Constrained System Design Problems

Prateek Jaiswal,  Harsh Honnappa,  Vinayak A. Rao ; PMLR 118:1-12

Variational Selective Autoencoder

Yu Gong,  Hossein Hajimirsadeghi,  Jiawei He,  Megha Nawhal,  Thibaut Durand,  Greg Mori ; PMLR 118:1-17

Bijectors.jl: Flexible transformations for probability distributions

Tor Erlend Fjelde,  Kai Xu,  Mohamed Tarek,  Sharan Yalburgi,  Hong Ge ; PMLR 118:1-17

MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy

Badr-Eddine Cherief-Abdellatif,  Pierre Alquier ; PMLR 118:1-21

GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models

Pavel Berkovich,  Eric Perim,  Wessel Bruinsma ; PMLR 118:1-14

Variational Predictive Information Bottleneck

Alexander A. Alemi ; PMLR 118:1-6

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