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Stochastic Normalizing Flows NeurIPS 2020 Stochastic Stein Discrepancies NeurIPS 2020 Switching Poisson Gamma Dynamical Systems IJCAI 2020 The Boomerang Sampler ICML 2020 Towards Scalable Bayesian Learning of Causal DAGs NeurIPS 2020 Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models ICLR 2020 Undirected Graphical Models as Approximate Posteriors ICML 2020 Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models NeurIPS 2020 Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020 Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge IJCAI 2020 Accelerated Flow for Probability Distributions ICML 2019 A Contrastive Divergence for Combining Variational Inference and MCMC ICML 2019 Adaptive MCMC via Combining Local Samplers AISTATS 2019 Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models AISTATS 2019 An Adaptive Empirical Bayesian Method for Sparse Deep Learning NeurIPS 2019 A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization JMLR 2019 A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes ICML 2019 Auxiliary Variational MCMC ICLR 2019 Bayesian Parameter Estimation for Nonlinear Dynamics Using Sensitivity Analysis IJCAI 2019 Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees JMLR 2019 Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior ICML 2019 Calibrated Approximate Bayesian Inference ICML 2019 CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling AAAI 2019 Deep Switch Networks for Generating Discrete Data and Language AISTATS 2019 Dependent relevance determination for smooth and structured sparse regression JMLR 2019