likelihood-free inference

Generative Bayesian Inference with GANs JMLR 2026 Learning Likelihood-Free Reference Priors ICML 2025 Approximate Bayesian Computation with Path Signatures UAI 2024 Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC JMLR 2024 Neural Bayes estimators for censored inference with peaks-over-threshold models JMLR 2024 Probabilistic Offline Policy Ranking with Approximate Bayesian Computation AAAI 2024 Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs AISTATS 2024 Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models ICML 2024 Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation NeurIPS 2024 Differentiable user models UAI 2023 Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models AISTATS 2023 Is Learning Summary Statistics Necessary for Likelihood-free Inference? ICML 2023 Kernel-Based Tests for Likelihood-Free Hypothesis Testing NeurIPS 2023 Minimax optimal testing by classification COLT 2023 Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference ICML 2023 Pseudo-Likelihood Inference NeurIPS 2023 A General Recipe for Likelihood-free Bayesian Optimization ICML 2022 Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation AISTATS 2022 Approximate Bayesian Computation via Classification JMLR 2022 Approximate Bayesian Computation with Domain Expert in the Loop ICML 2022 Score Matched Neural Exponential Families for Likelihood-Free Inference JMLR 2022 Unifying Likelihood-free Inference with Black-box Optimization and Beyond ICLR 2022 Benchmarking Simulation-Based Inference AISTATS 2021 Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods NeurIPS 2021 Likelihood-free Out-of-Distribution Detection with Invertible Generative Models IJCAI 2021