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

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

12 September 2018

Mert Gurbuzbalaban

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

19 / 19 papers shown

Title
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks Hao Sun Li Shen Qihuang Zhong Liang Ding Shi-Yong Chen Jingwei Sun Jing Li Guangzhong Sun Dacheng Tao 98 34 0 01 Mar 2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case Tim Johnston Iosif Lytras Sotirios Sabanis 79 9 0 19 Jan 2023
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network Siqi Liang Y. Sun F. Liang BDL 71 11 0 09 Oct 2022
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 103 7 0 02 Jan 2022
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev Adam D. Cobb T. Javidi Brian Jalaian 88 4 0 15 Jul 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions Alessandro Barp So Takao M. Betancourt Alexis Arnaudon Mark Girolami 72 17 0 06 May 2021
The shifted ODE method for underdamped Langevin MCMC James Foster Terry Lyons Harald Oberhauser 91 16 0 10 Jan 2021
State-Dependent Temperature Control for Langevin Diffusions Xuefeng Gao Z. Xu X. Zhou 94 28 0 15 Nov 2020
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling Difan Zou Pan Xu Quanquan Gu 101 36 0 19 Oct 2020
Structured Logconcave Sampling with a Restricted Gaussian Oracle Y. Lee Ruoqi Shen Kevin Tian 79 73 0 07 Oct 2020
Quantum algorithms for escaping from saddle points Chenyi Zhang Jiaqi Leng Tongyang Li 86 20 0 20 Jul 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks Umut Simsekli Ozan Sener George Deligiannidis Murat A. Erdogdu 86 56 0 16 Jun 2020
The Heavy-Tail Phenomenon in SGD Mert Gurbuzbalaban Umut Simsekli Lingjiong Zhu 59 130 0 08 Jun 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC Ruqi Zhang A. Feder Cooper Christopher De Sa 81 18 0 29 Feb 2020
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise Umut Simsekli Lingjiong Zhu Yee Whye Teh Mert Gurbuzbalaban 82 50 0 13 Feb 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization Ömer Deniz Akyildiz Sotirios Sabanis 97 17 0 13 Feb 2020
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning Huy N. Chau M. Rásonyi 71 10 0 25 Mar 2019
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Bin Shi S. Du Michael I. Jordan Weijie J. Su 68 264 0 21 Oct 2018
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions N. Aybat Alireza Fallah Mert Gurbuzbalaban Asuman Ozdaglar 77 59 0 27 May 2018