reparameterization gradient

Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing AISTATS 2024 Reparameterized Variational Rejection Sampling AISTATS 2024 Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators ICML 2023 Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms NeurIPS 2023 On the Convergence of Black-Box Variational Inference NeurIPS 2023 Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms NeurIPS 2022 Variational Marginal Particle Filters AISTATS 2022 A unified view of likelihood ratio and reparameterization gradients AISTATS 2021 MCMC Variational Inference via Uncorrected Hamiltonian Annealing NeurIPS 2021 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021 Slice Sampling Reparameterization Gradients NeurIPS 2021 Approximation Based Variance Reduction for Reparameterization Gradients NeurIPS 2020 Provable Gradient Variance Guarantees for Black-Box Variational Inference NeurIPS 2019 Reparameterizable Subset Sampling via Continuous Relaxations IJCAI 2019 PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos ICML 2018 Quasi-Monte Carlo Variational Inference ICML 2018 Reparameterization Gradient for Non-differentiable Models NeurIPS 2018 Reparameterizing the Birkhoff Polytope for Variational Permutation Inference AISTATS 2018 Perturbative Black Box Variational Inference NeurIPS 2017 Reducing Reparameterization Gradient Variance NeurIPS 2017 Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 The Generalized Reparameterization Gradient NeurIPS 2016 Decoupled Variational Gaussian Inference NeurIPS 2014