posterior approximation

Easy Variational Inference for Categorical Models via an Independent Binary Approximation ICML 2022 Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders CVPR 2022 On the detrimental effect of invariances in the likelihood for variational inference NeurIPS 2022 Posterior and Computational Uncertainty in Gaussian Processes NeurIPS 2022 Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks NeurIPS 2022 Structured Stochastic Gradient MCMC ICML 2022 Structured variational inference in Bayesian state-space models AISTATS 2022 Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation NeurIPS 2022 Transfer Learning with Gaussian Processes for Bayesian Optimization AISTATS 2022 Transformers Can Do Bayesian Inference ICLR 2022 Variational Inference for Infinitely Deep Neural Networks ICML 2022 Variational inference via Wasserstein gradient flows NeurIPS 2022 Variational Inference with Locally Enhanced Bounds for Hierarchical Models ICML 2022 Bayesian Deep Learning via Subnetwork Inference ICML 2021 Challenges and Opportunities in High Dimensional Variational Inference NeurIPS 2021 Functional Variational Inference based on Stochastic Process Generators NeurIPS 2021 Measure Transport with Kernel Stein Discrepancy AISTATS 2021 Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference ICML 2021 Repulsive Deep Ensembles are Bayesian NeurIPS 2021 Structured Dropout Variational Inference for Bayesian Neural Networks NeurIPS 2021 Variational inference with continuously-indexed normalizing flows UAI 2021 Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation EMNLP 2020 Bayesian Pseudocoresets NeurIPS 2020 CCA-Flow: Deep Multi-view Subspace Learning with Inverse Autoregressive Flow ACML 2020 Decision-Making with Auto-Encoding Variational Bayes NeurIPS 2020