approximate inference

Approximate implication with d-separation UAI 2021 Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances AISTATS 2021 Context-Specific Likelihood Weighting AISTATS 2021 Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces NeurIPS 2021 Normalizing Flows for Probabilistic Modeling and Inference JMLR 2021 On the difficulty of unbiased alpha divergence minimization ICML 2021 Partition Function Estimation: A Quantitative Study IJCAI 2021 Post-hoc loss-calibration for Bayesian neural networks UAI 2021 Probabilistic DAG search UAI 2021 Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables AAAI 2021 Streaming Bayesian Deep Tensor Factorization ICML 2021 Tractable Approximate Gaussian Inference for Bayesian Neural Networks JMLR 2021 VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning JMLR 2021 AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network EMNLP 2020 Amortized Inference of Variational Bounds for Learning Noisy-OR AISTATS 2020 Amortized variance reduction for doubly stochastic objective UAI 2020 An Efficient Low-Rank Tensors Representation for Algorithms in Complex Probabilistic Graphical Models PGM 2020 Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions AISTATS 2020 Approximate Inference with Wasserstein Gradient Flows AISTATS 2020 Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry IJCAI 2020 Asymptotic Consistency of α-Rényi-Approximate Posteriors JMLR 2020 Correcting Predictions for Approximate Bayesian Inference AAAI 2020 CREMA: A Java Library for Credal Network Inference PGM 2020 Deep Structured Mixtures of Gaussian Processes AISTATS 2020 Distortion estimates for approximate Bayesian inference UAI 2020