conftrace_

posterior approximation

132 papers

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Co-occurring keywords

variational inference (2069) bayesian inference (1907) bayesian neural network (201) markov chain monte carlo (725) uncertainty quantification (1393) approximate inference (253) amortized inference (102) gaussian process (1200) generative model (2892) normalizing flow (354)

Papers

Decision-Making with Auto-Encoding Variational Bayes NIPS 2020
Radial and Directional Posteriors for Bayesian Deep Learning AAAI 2020
Uncertainty in Neural Networks: Approximately Bayesian Ensembling AISTATS 2020
Deep Gaussian Processes with Importance-Weighted Variational Inference ICML 2019
Conditional Expectation Propagation UAI 2019
Subspace Inference for Bayesian Deep Learning UAI 2019
Doubly Semi-Implicit Variational Inference AISTATS 2019
Bayesian Posterior Approximation via Greedy Particle Optimization AAAI 2019
Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019
Universal Boosting Variational Inference NIPS 2019
Variational Laplace Autoencoders ICML 2019
Assumed Density Filtering Q-learning IJCAI 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning NIPS 2019
Variational Bayesian Decision-making for Continuous Utilities NIPS 2019
Sparse Variational Inference: Bayesian Coresets from Scratch NIPS 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation NIPS 2019
Copula-like Variational Inference NIPS 2019
Multi-Source Neural Variational Inference AAAI 2019
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models ICML 2018
Stein Points ICML 2018
Inference Suboptimality in Variational Autoencoders ICML 2018
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent ICML 2018
Boosting Variational Inference: an Optimization Perspective AISTATS 2018
Efficient Bayesian Methods for Counting Processes in Partially Observable Environments AISTATS 2018