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

[edit]

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

[bib][citeproc]

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 ecient 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

subscribe via RSS

This site last compiled Mon, 06 Apr 2020 15:00:22 +0000
Github Account Copyright © PMLR 2020. All rights reserved.