sampling algorithm

Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization NeurIPS 2022 Inference and Sampling for Archimax Copulas NeurIPS 2022 More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees ICML 2022 Natural gradient enables fast sampling in spiking neural networks NeurIPS 2022 Nonparametric Involutive Markov Chain Monte Carlo ICML 2022 On Scalable Testing of Samplers NeurIPS 2022 Path Integral Sampler: A Stochastic Control Approach For Sampling ICLR 2022 Private Convex Optimization via Exponential Mechanism COLT 2022 Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods COLT 2022 Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes ICML 2022 Top Two Algorithms Revisited NeurIPS 2022 Differentially Private Sampling from Distributions NeurIPS 2021 Efficient constrained sampling via the mirror-Langevin algorithm NeurIPS 2021 Efficient Sampling of Dependency Structure EMNLP 2021 Geometric convergence of elliptical slice sampling ICML 2021 High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021 Neural Regret-Matching for Distributed Constraint Optimization Problems IJCAI 2021 Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers AISTATS 2021 On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness COLT 2021 Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm COLT 2021 Perturb-and-max-product: Sampling and learning in discrete energy-based models NeurIPS 2021 Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search (Extended Abstract) IJCAI 2021 Sampling Partial Acyclic Orientations in Chordal Graphs by the Lovasz Local Lemma (Student Abstract) AAAI 2021 An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks AAAI 2020 A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation AACL 2020