approximate inference

Flexible Approximate Inference via Stratified Normalizing Flows UAI 2020 Inter-domain Deep Gaussian Processes ICML 2020 Learning Behaviors with Uncertain Human Feedback UAI 2020 Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting AAAI 2020 MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search AISTATS 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks NeurIPS 2020 Probabilistic Graphical Models with Neural Networks in InferPy PGM 2020 Quantile Propagation for Wasserstein-Approximate Gaussian Processes NeurIPS 2020 Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2020 Stein Variational Inference for Discrete Distributions AISTATS 2020 Stochastic Stein Discrepancies NeurIPS 2020 Towards Consistent Variational Auto-Encoding (Student Abstract) AAAI 2020 Trust-Region Variational Inference with Gaussian Mixture Models JMLR 2020 Uncertainty in Neural Networks: Approximately Bayesian Ensembling AISTATS 2020 Variable Skipping for Autoregressive Range Density Estimation ICML 2020 Walsh-Hadamard Variational Inference for Bayesian Deep Learning NeurIPS 2020 Amortized Bethe Free Energy Minimization for Learning MRFs NeurIPS 2019 Approximate Inference in Structured Instances with Noisy Categorical Observations UAI 2019 Approximate Inference Turns Deep Networks into Gaussian Processes NeurIPS 2019 A Simple Baseline for Bayesian Uncertainty in Deep Learning NeurIPS 2019 A Weighted Mini-Bucket Bound for Solving Influence Diagram UAI 2019 Bayesian leave-one-out cross-validation for large data ICML 2019 Benchmarking Approximate Inference Methods for Neural Structured Prediction NAACL 2019 Block Belief Propagation for Parameter Learning in Markov Random Fields AAAI 2019 Conditional Expectation Propagation UAI 2019