posterior approximation

Doubly Semi-Implicit Variational Inference AISTATS 2019 Multi-Source Neural Variational Inference AAAI 2019 Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019 Sparse Variational Inference: Bayesian Coresets from Scratch NeurIPS 2019 Subspace Inference for Bayesian Deep Learning UAI 2019 Universal Boosting Variational Inference NeurIPS 2019 Variational Bayesian Decision-making for Continuous Utilities NeurIPS 2019 Variational Laplace Autoencoders ICML 2019 Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent ICML 2018 Boosting Variational Inference: an Optimization Perspective AISTATS 2018 CRVI: Convex Relaxation for Variational Inference ICML 2018 Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling ICLR 2018 Efficient Bayesian Methods for Counting Processes in Partially Observable Environments AISTATS 2018 Faithful Inversion of Generative Models for Effective Amortized Inference NeurIPS 2018 Inference Suboptimality in Variational Autoencoders ICML 2018 Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models ICML 2018 PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits NeurIPS 2018 Stein Points ICML 2018 Variational Sequential Monte Carlo AISTATS 2018 Automatic Differentiation Variational Inference JMLR 2017 Expectation Propagation for t-Exponential Family Using q-Algebra NeurIPS 2017 PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference NeurIPS 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 Variational Boosting: Iteratively Refining Posterior Approximations ICML 2017 Hierarchical Variational Models ICML 2016