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[1] Pierre E. Jacob,et al. Estimating Convergence of Markov chains with L-Lag Couplings , 2019, NeurIPS.
[2] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[3] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[4] John O'Leary,et al. Unbiased Markov chain Monte Carlo with couplings , 2017, 1708.03625.
[5] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[6] Peter W. Glynn,et al. Exact estimation for Markov chain equilibrium expectations , 2014, Journal of Applied Probability.
[7] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[8] Xi Chen,et al. PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications , 2017, ICLR.
[9] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[10] Ruslan Salakhutdinov,et al. On the quantitative analysis of deep belief networks , 2008, ICML '08.
[11] David Duvenaud,et al. Reinterpreting Importance-Weighted Autoencoders , 2017, ICLR.
[12] Xiao Wang,et al. Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models , 2020, ICLR.
[13] Justin Domke,et al. Importance Weighting and Variational Inference , 2018, NeurIPS.
[14] A. Doucet,et al. Particle Markov chain Monte Carlo methods , 2010 .
[15] Arnaud Doucet,et al. Unbiased Smoothing using Particle Independent Metropolis-Hastings , 2019, AISTATS.
[16] Christophe Andrieu,et al. Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers , 2013, 1312.6432.
[17] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[18] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[19] Noah D. Goodman,et al. Amortized Inference in Probabilistic Reasoning , 2014, CogSci.
[20] Yee Whye Teh,et al. Tighter Variational Bounds are Not Necessarily Better , 2018, ICML.
[21] Fredrik Lindsten,et al. Smoothing With Couplings of Conditional Particle Filters , 2017, Journal of the American Statistical Association.
[22] A. Doucet,et al. Controlled sequential Monte Carlo , 2017, The Annals of Statistics.
[23] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[24] T. Lindvall. Lectures on the Coupling Method , 1992 .
[25] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[26] Radford M. Neal,et al. Sampling Latent States for High-Dimensional Non-Linear State Space Models with the Embedded HMM Method , 2016, Bayesian Analysis.
[27] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[28] P. Moral,et al. Sequential Monte Carlo samplers , 2002, cond-mat/0212648.
[29] George Tucker,et al. Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives , 2019, ICLR.
[30] Miguel Lázaro-Gredilla,et al. Doubly Stochastic Variational Bayes for non-Conjugate Inference , 2014, ICML.
[31] Fredrik Lindsten,et al. Markovian Score Climbing: Variational Inference with KL(p||q) , 2020, NeurIPS.
[32] Xiao-Li Meng,et al. Double Happiness: Enhancing the Coupled Gains of L-lag Coupling via Control Variates. , 2020, 2008.12662.
[33] Ryan P. Adams,et al. SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models , 2020, ICLR.
[34] Arnaud Doucet,et al. Unbiased Markov chain Monte Carlo for intractable target distributions , 2020, Electronic Journal of Statistics.