hamiltonian monte carlo

Shadow Manifold Hamiltonian Monte Carlo AISTATS 2021 Skew-symmetrically perturbed gradient flow for convex optimization ACML 2021 What Are Bayesian Neural Network Posteriors Really Like? ICML 2021 Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients JMLR 2020 Hamiltonian Monte Carlo Swindles AISTATS 2020 Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NeurIPS 2020 Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations NeurIPS 2020 Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo COLT 2020 Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables NeurIPS 2020 Robustness of Bayesian Neural Networks to Gradient-Based Attacks NeurIPS 2020 Auxiliary Variational MCMC ICLR 2019 Co-Generation with GANs using AIS based HMC NeurIPS 2019 Exponential Family Estimation via Adversarial Dynamics Embedding NeurIPS 2019 Hamiltonian Monte Carlo with Energy Conserving Subsampling JMLR 2019 LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models AISTATS 2019 Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators AISTATS 2019 ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees ICLR 2019 Pseudo-Extended Markov chain Monte Carlo NeurIPS 2019 SpHMC: Spectral Hamiltonian Monte Carlo AAAI 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction NeurIPS 2019 A Spectral Approach to Gradient Estimation for Implicit Distributions ICML 2018 Bayesian Tactile Exploration for Compliant Docking With Uncertain Shapes RSS 2018 Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo NeurIPS 2018 Efficient and principled score estimation with Nyström kernel exponential families AISTATS 2018 Generalizing Hamiltonian Monte Carlo with Neural Networks ICLR 2018