posterior approximation

Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference AISTATS 2024 Simulation-Based Stacking AISTATS 2024 Streaming Bayes GFlowNets NeurIPS 2024 A Targeted Accuracy Diagnostic for Variational Approximations AISTATS 2023 Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023 Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach ICLR 2023 GPEX, A Framework For Interpreting Artificial Neural Networks NeurIPS 2023 Gradient-Free Kernel Stein Discrepancy NeurIPS 2023 Implicit Variational Inference for High-Dimensional Posteriors NeurIPS 2023 Influence-Aware Attention for Multivariate Temporal Point Processes CLeaR 2023 Jana: Jointly amortized neural approximation of complex Bayesian models UAI 2023 L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference NeurIPS 2023 MixFlows: principled variational inference via mixed flows ICML 2023 Non-reversible Parallel Tempering for Deep Posterior Approximation AAAI 2023 On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making NeurIPS 2023 Particle-based Variational Inference with Preconditioned Functional Gradient Flow ICLR 2023 Riemannian Laplace approximations for Bayesian neural networks NeurIPS 2023 Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data ICML 2023 The Bayesian Learning Rule JMLR 2023 Variational Inference with Gaussian Score Matching NeurIPS 2023 An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference JMLR 2022 A Probabilistic Framework for Land Deformation Prediction (Student Abstract) AAAI 2022 Bayesian inference via sparse Hamiltonian flows NeurIPS 2022 Bayesian structure learning with generative flow networks UAI 2022 Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference AISTATS 2022