Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration

12 September 2018

Papers citing "Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration"

17 / 17 papers shown

Title
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants Chris Junchi Li 23 0 0 22 Apr 2024
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping Umut Simsekli Mert Gurbuzbalaban S. Yıldırım Lingjiong Zhu 38 2 0 04 Mar 2024
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent Lingjiong Zhu Mert Gurbuzbalaban Anant Raj Umut Simsekli 24 6 0 20 May 2023
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize Mert Gurbuzbalaban Yuanhan Hu Umut Simsekli Lingjiong Zhu LRM 11 1 0 10 Feb 2023
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions Anant Raj Lingjiong Zhu Mert Gurbuzbalaban Umut Simsekli 21 15 0 27 Jan 2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case Tim Johnston Iosif Lytras Sotirios Sabanis 30 8 0 19 Jan 2023
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 25 7 0 02 Jan 2022
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev Adam D. Cobb T. Javidi Brian Jalaian 51 4 0 15 Jul 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions Alessandro Barp So Takao M. Betancourt Alexis Arnaudon Mark Girolami 20 17 0 06 May 2021
The shifted ODE method for underdamped Langevin MCMC James Foster Terry Lyons Harald Oberhauser 16 16 0 10 Jan 2021
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks Umut Simsekli Ozan Sener George Deligiannidis Murat A. Erdogdu 41 55 0 16 Jun 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization Ömer Deniz Akyildiz Sotirios Sabanis 35 17 0 13 Feb 2020
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning Huy N. Chau M. Rásonyi 19 10 0 25 Mar 2019
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Bin Shi S. Du Michael I. Jordan Weijie J. Su 17 251 0 21 Oct 2018
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen Nan Ding Lawrence Carin 34 158 0 21 Oct 2016
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 102 1,152 0 04 Mar 2015
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,262 0 09 Jun 2012