likelihood-free inference

Neural Approximate Sufficient Statistics for Implicit Models ICLR 2021 Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference AISTATS 2021 Truncated Marginal Neural Ratio Estimation NeurIPS 2021 γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator AISTATS 2021 Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting ICML 2020 Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems ICML 2020 Likelihood-free MCMC with Amortized Approximate Ratio Estimators ICML 2020 On Contrastive Learning for Likelihood-free Inference ICML 2020 Adversarial Variational Optimization of Non-Differentiable Simulators AISTATS 2019 A Generative Adversarial Density Estimator CVPR 2019 Automatic Posterior Transformation for Likelihood-Free Inference ICML 2019 Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference AISTATS 2019 BayesSim: Adaptive Domain Randomization Via Probabilistic Inference for Robotics Simulators RSS 2019 Efficient Bayesian Experimental Design for Implicit Models AISTATS 2019 Forward Amortized Inference for Likelihood-Free Variational Marginalization AISTATS 2019 Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows AISTATS 2019 A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks NeurIPS 2018 Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy AISTATS 2018 ELFI: Engine for Likelihood-Free Inference JMLR 2018 A-NICE-MC: Adversarial Training for MCMC NeurIPS 2017 Flexible statistical inference for mechanistic models of neural dynamics NeurIPS 2017 Wasserstein Learning of Deep Generative Point Process Models NeurIPS 2017 Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models JMLR 2016 Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation NeurIPS 2016 Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference NeurIPS 2015