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ORCIDAABI 2019: Vancouver, BC, Canada
- Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang:
Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. Proceedings of Machine Learning Research 118, PMLR 2020 - Lily H. Zhang, Michael C. Hughes:
Rapid Model Comparison by Amortizing Across Models. 1-11 - Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. 1-17 - Kai Xu, Hong Ge, Will Tebbutt, Mohamed Tarek, Martin Trapp, Zoubin Ghahramani:
AdvancedHMC.jl: A robust, modular and e cient implementation of advanced HMC algorithms. 1-10 - Mark van der Wilk, S. T. John, Artem Artemev, James Hensman:
Variational Gaussian Process Models without Matrix Inverses. 1-9 - Ravid Shwartz-Ziv, Alexander A. Alemi:
Information in Infinite Ensembles of Infinitely-Wide Neural Networks. 1-17 - Rishit Sheth, Roni Khardon:
Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models. 1-18 - Yura Perov, Logan Graham, Kostis Gourgoulias, Jonathan G. Richens, Ciarán M. Lee, Adam Baker, Saurabh Johri:
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming. 1-36 - Michael Pearce:
The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels. 1-12 - Ari Pakman, Yueqi Wang, Liam Paninski:
Neural Permutation Processes. 1-7 - Gonzalo E. Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski:
Sinkhorn Permutation Variational Marginal Inference. 1-9 - Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt:
Improving Sequential Latent Variable Models with Autoregressive Flows. 1-16 - Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. 1-8 - Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud:
Scalable Gradients and Variational Inference for Stochastic Differential Equations. 1-28 - Vidhi Lalchand, Carl Edward Rasmussen:
Approximate Inference for Fully Bayesian Gaussian Process Regression. 1-12 - He Jia, Uros Seljak:
Normalizing Constant Estimation with Gaussianized Bridge Sampling. 1-14 - Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao:
Variational Bayesian Methods for Stochastically Constrained System Design Problems. 1-12 - Yu Gong, Hossein Hajimirsadeghi, Jiawei He, Megha Nawhal, Thibaut Durand, Greg Mori:
Variational Selective Autoencoder. 1-17 - Tor Erlend Fjelde, Kai Xu, Mohamed Tarek, Sharan Yalburgi, Hong Ge:
Bijectors.jl: Flexible transformations for probability distributions. 1-17 - Badr-Eddine Chérief-Abdellatif, Pierre Alquier:
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy. 1-21 - Pavel Berkovich, Eric Perim, Wessel P. Bruinsma:
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models. 1-14 - Alexander A. Alemi:
Variational Predictive Information Bottleneck. 1-6
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