stein variational gradient descent

A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1 ICML 2022 Grassmann Stein Variational Gradient Descent AISTATS 2022 Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning AISTATS 2022 Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent NeurIPS 2022 Sampling with Mirrored Stein Operators ICLR 2022 Stochastic Multiple Target Sampling Gradient Descent NeurIPS 2022 Understanding the Variance Collapse of SVGD in High Dimensions ICLR 2022 De-randomizing MCMC dynamics with the diffusion Stein operator NeurIPS 2021 Dual Online Stein Variational Inference for Control and Dynamics RSS 2021 Kernel Stein Discrepancy Descent ICML 2021 Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent NeurIPS 2021 Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent NeurIPS 2021 Sampling with Trusthworthy Constraints: A Variational Gradient Framework NeurIPS 2021 A Non-Asymptotic Analysis for Stein Variational Gradient Descent NeurIPS 2020 Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity CoRL 2020 Implicit Generative Modeling for Efficient Exploration ICML 2020 Learning Manifold Implicitly via Explicit Heat-Kernel Learning NeurIPS 2020 Projected Stein Variational Gradient Descent NeurIPS 2020 Stein Variational Inference for Discrete Distributions AISTATS 2020 Stein Variational Model Predictive Control CoRL 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Stochastic Stein Discrepancies NeurIPS 2020 SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NeurIPS 2020 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020 Variance Reduction in Stochastic Particle-Optimization Sampling ICML 2020