Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

20 July 2017

Quanquan Gu

Papers citing "Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization"

36 / 36 papers shown

Title
Random Reshuffling for Stochastic Gradient Langevin Dynamics Luke Shaw Peter A. Whalley 135 3 0 28 Jan 2025
Provable Accuracy Bounds for Hybrid Dynamical Optimization and Sampling Matthew Burns Qingyuan Hou Michael Huang 361 1 0 08 Oct 2024
Energy-based Hopfield Boosting for Out-of-Distribution Detection Claus Hofmann Simon Schmid Bernhard Lehner Daniel Klotz Sepp Hochreiter OODD 82 9 0 14 May 2024
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias Lu Yu Krishnakumar Balasubramanian S. Volgushev Murat A. Erdogdu 87 51 0 14 Jun 2020
An Adaptive Empirical Bayesian Method for Sparse Deep Learning Wei Deng Xiao Zhang F. Liang Guang Lin BDL 105 44 0 23 Oct 2019
Global Non-convex Optimization with Discretized Diffusions Murat A. Erdogdu Lester W. Mackey Ohad Shamir 66 105 0 29 Oct 2018
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization Umut Simsekli Çağatay Yıldız T. H. Nguyen G. Richard A. Cemgil 33 22 0 07 Jun 2018
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference Zhize Li Tianyi Zhang Shuyu Cheng Jun Yu Li Jian Li BDL 49 18 0 29 Mar 2018
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability Belinda Tzen Tengyuan Liang Maxim Raginsky 37 32 0 18 Feb 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo Niladri S. Chatterji Nicolas Flammarion Yian Ma Peter L. Bartlett Michael I. Jordan 60 87 0 15 Feb 2018
Stochastic Variance-Reduced Cubic Regularized Newton Method Dongruo Zhou Pan Xu Quanquan Gu ODL 47 45 0 13 Feb 2018
Stochastic Variance-Reduced Hamilton Monte Carlo Methods Difan Zou Pan Xu Quanquan Gu BDL 48 31 0 13 Feb 2018
Log-concave sampling: Metropolis-Hastings algorithms are fast Raaz Dwivedi Yuansi Chen Martin J. Wainwright Bin Yu 66 254 0 08 Jan 2018
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent Chi Jin Praneeth Netrapalli Michael I. Jordan ODL 59 261 0 28 Nov 2017
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo Rong Ge Holden Lee Andrej Risteski 64 53 0 07 Oct 2017
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient A. Dalalyan Avetik G. Karagulyan 65 296 0 29 Sep 2017
Underdamped Langevin MCMC: A non-asymptotic analysis Xiang Cheng Niladri S. Chatterji Peter L. Bartlett Michael I. Jordan 82 297 0 12 Jul 2017
Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent A. Dalalyan BDL 45 174 0 16 Apr 2017
How to Escape Saddle Points Efficiently Chi Jin Rong Ge Praneeth Netrapalli Sham Kakade Michael I. Jordan ODL 197 835 0 02 Mar 2017
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics Yuchen Zhang Percy Liang Moses Charikar 61 236 0 18 Feb 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 70 521 0 13 Feb 2017
The Power of Normalization: Faster Evasion of Saddle Points Kfir Y. Levy 65 108 0 15 Nov 2016
Finding Approximate Local Minima Faster than Gradient Descent Naman Agarwal Zeyuan Allen-Zhu Brian Bullins Elad Hazan Tengyu Ma 78 83 0 03 Nov 2016
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen Nan Ding Lawrence Carin 66 161 0 21 Oct 2016
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 74 33 0 05 May 2016
Stochastic Variance Reduction for Nonconvex Optimization Sashank J. Reddi Ahmed S. Hefny S. Sra Barnabás Póczós Alex Smola 92 600 0 19 Mar 2016
Variance Reduction for Faster Non-Convex Optimization Zeyuan Allen-Zhu Elad Hazan ODL 110 392 0 17 Mar 2016
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 64 412 0 17 Jul 2015
Sampling from a log-concave distribution with Projected Langevin Monte Carlo Sébastien Bubeck Ronen Eldan Joseph Lehec 67 139 0 09 Jul 2015
A Complete Recipe for Stochastic Gradient MCMC Yian Ma Tianqi Chen E. Fox BDL SyDa 60 486 0 15 Jun 2015
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition Rong Ge Furong Huang Chi Jin Yang Yuan 132 1,058 0 06 Mar 2015
Theoretical guarantees for approximate sampling from smooth and log-concave densities A. Dalalyan 66 514 0 23 Dec 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 104 907 0 17 Feb 2014
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 120 1,547 0 22 Sep 2013
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring S. Ahn Anoop Korattikara Balan Max Welling 77 306 0 27 Jun 2012
MCMC using Hamiltonian dynamics Radford M. Neal 288 3,276 0 09 Jun 2012