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stochastic gradient langevin dynamics

49 papers

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Also known as

SGLD

Co-occurring keywords

bayesian inference (1907) markov chain monte carlo (725) generalization bound (652) variance reduction (523) posterior distribution (259) non-convex optimization (547) information theory (491) uncertainty quantification (1393) differential privacy (1016) bayesian neural network (201)

Papers

Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes NIPS 2020
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs ICML 2020
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics JMLR 2020
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics ICML 2020
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics AAAI 2020
Bayesian Learning for Neural Dependency Parsing NAACL 2019
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates NIPS 2019
Online sampling from log-concave distributions NIPS 2019
A Bayesian Perspective on the Deep Image Prior CVPR 2019
Analysis of Langevin Monte Carlo via Convex Optimization JMLR 2019
Data-dependent PAC-Bayes priors via differential privacy NIPS 2018
Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing COLT 2018
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints COLT 2018
Adversarial Distillation of Bayesian Neural Network Posteriors ICML 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors ICML 2018
The promises and pitfalls of Stochastic Gradient Langevin Dynamics NIPS 2018
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics COLT 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis COLT 2017
Variance Reduction in Stochastic Gradient Langevin Dynamics NIPS 2016
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics JMLR 2016
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics JMLR 2016
Bayesian dark knowledge NIPS 2015
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) ICML 2015
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators NIPS 2015