posterior approximation

Distortion estimates for approximate Bayesian inference UAI 2020 Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support ICML 2020 Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020 Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020 Gradient Boosted Normalizing Flows NeurIPS 2020 Implicit Generative Modeling for Efficient Exploration ICML 2020 Layering-MCMC for Structure Learning in Bayesian Networks UAI 2020 Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data AISTATS 2020 Radial and Directional Posteriors for Bayesian Deep Learning AAAI 2020 Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning AISTATS 2020 Recursive Inference for Variational Autoencoders NeurIPS 2020 Robust, Accurate Stochastic Optimization for Variational Inference NeurIPS 2020 Robust Optimisation Monte Carlo AISTATS 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Uncertainty in Neural Networks: Approximately Bayesian Ensembling AISTATS 2020 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020 Variational Bayesian Unlearning NeurIPS 2020 A Simple Baseline for Bayesian Uncertainty in Deep Learning NeurIPS 2019 Assumed Density Filtering Q-learning IJCAI 2019 Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019 Bayesian Posterior Approximation via Greedy Particle Optimization AAAI 2019 Conditional Expectation Propagation UAI 2019 Copula-like Variational Inference NeurIPS 2019 Deep Gaussian Processes with Importance-Weighted Variational Inference ICML 2019 Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation NeurIPS 2019