gradient estimator

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables ICML 2019 Calibrated Stochastic Gradient Descent for Convolutional Neural Networks AAAI 2019 Complexities in Projection-Free Stochastic Non-convex Minimization AISTATS 2019 Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives ICLR 2019 Efficient optimization of loops and limits with randomized telescoping sums ICML 2019 Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning NeurIPS 2019 Pathwise Derivatives for Multivariate Distributions AISTATS 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction NeurIPS 2019 Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR 2019 Variance Reduction for Matrix Games NeurIPS 2019 Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization IJCAI 2019 Demystifying MMD GANs ICLR 2018 Gradient Estimators for Implicit Models ICLR 2018 Implicit Reparameterization Gradients NeurIPS 2018 Tighter Variational Bounds are Not Necessarily Better ICML 2018 Total stochastic gradient algorithms and applications in reinforcement learning NeurIPS 2018 Variational Rejection Sampling AISTATS 2018 An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback JMLR 2017 Evaluating the Variance of Likelihood-Ratio Gradient Estimators ICML 2017 REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NeurIPS 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NeurIPS 2016 Variational Inference for Monte Carlo Objectives ICML 2016