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2020

Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019Cheng Zhang 0005, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang, editors, Volume 118 of Proceedings of Machine Learning Research, PMLR, 2020. [doi]

2019

Variational Predictive Information BottleneckAlexander A. Alemi. aabi 2020: 1-6 [doi]

GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process ModelsPavel Berkovich, Eric Perim, Wessel Bruinsma. aabi 2020: 1-14 [doi]

MMD-Bayes: Robust Bayesian Estimation via Maximum Mean DiscrepancyBadr-Eddine Chérief-Abdellatif, Pierre Alquier. aabi 2020: 1-21 [doi]

Bijectors.jl: Flexible transformations for probability distributionsTor Erlend Fjelde, Kai Xu, Mohamed Tarek, Sharan Yalburgi, Hong Ge. aabi 2020: 1-17 [doi]

Variational Selective AutoencoderYu Gong, Hossein Hajimirsadeghi, Jiawei He, Megha Nawhal, Thibaut Durand, Greg Mori. aabi 2020: 1-17 [doi]

Variational Bayesian Methods for Stochastically Constrained System Design ProblemsPrateek Jaiswal, Harsha Honnappa, Vinayak A. Rao. aabi 2020: 1-12 [doi]

Normalizing Constant Estimation with Gaussianized Bridge SamplingHe Jia, Uros Seljak. aabi 2020: 1-14 [doi]

Approximate Inference for Fully Bayesian Gaussian Process RegressionVidhi Lalchand, Carl Edward Rasmussen. aabi 2020: 1-12 [doi]

Scalable Gradients and Variational Inference for Stochastic Differential EquationsXuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Kristjanson Duvenaud. aabi 2020: 1-28 [doi]

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous MarginalsChao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang. aabi 2020: 1-8 [doi]

Improving Sequential Latent Variable Models with Autoregressive FlowsJoseph Marino, Lei Chen, Jiawei He, Stephan Mandt. aabi 2020: 1-16 [doi]

Sinkhorn Permutation Variational Marginal InferenceGonzalo E. Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski. aabi 2020: 1-9 [doi]

Neural Permutation ProcessesAri Pakman, Yueqi Wang, Liam Paninski. aabi 2020: 1-7 [doi]

The Gaussian Process Prior VAE for Interpretable Latent Dynamics from PixelsMichael Pearce. aabi 2020: 1-12 [doi]

MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingYura Perov, Logan Graham, Kostis Gourgoulias, Jonathan G. Richens, Ciarán M. Lee, Adam Baker, Saurabh Johri. aabi 2020: 1-36 [doi]

Symposium on Advances in Approximate Bayesian Inference, AABI 2018, Montréal, QC, Canada, December 2, 2018Francisco J. R. Ruiz, Cheng Zhang 0005, Dawen Liang, Thang D. Bui, editors, Volume 96 of Proceedings of Machine Learning Research, PMLR, 2019. [doi]

Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process ModelsRishit Sheth, Roni Khardon. aabi 2020: 1-18 [doi]

Information in Infinite Ensembles of Infinitely-Wide Neural NetworksRavid Shwartz-Ziv, Alexander A. Alemi. aabi 2020: 1-17 [doi]

Variational Gaussian Process Models without Matrix InversesMark van der Wilk, S. T. John, Artem Artemev, James Hensman. aabi 2020: 1-9 [doi]

AdvancedHMC.jl: A robust, modular and e cient implementation of advanced HMC algorithmsKai Xu, Hong Ge, Will Tebbutt, Mohamed Tarek, Martin Trapp 0001, Zoubin Ghahramani. aabi 2020: 1-10 [doi]

Characterizing and Avoiding Problematic Global Optima of Variational AutoencodersYaniv Yacoby, Weiwei Pan, Finale Doshi-Velez. aabi 2020: 1-17 [doi]

Rapid Model Comparison by Amortizing Across ModelsLily H. Zhang, Michael C. Hughes. aabi 2020: 1-11 [doi]

2018

An Exploration of Acquisition and Mean Functions in Variational Bayesian Monte CarloLuigi Acerbi. aabi 2019: 1-10 [doi]

Consistency of ELBO maximization for model selectionBadr-Eddine Chérief-Abdellatif. aabi 2019: 11-31 [doi]

Likelihood-free inference with emulator networksJan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke. aabi 2019: 32-53 [doi]

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