Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

13 February 2017

Papers citing "Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis"

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Title
Interacting Contour Stochastic Gradient Langevin Dynamics Wei Deng Siqi Liang Botao Hao Guang Lin F. Liang BDL 26 10 0 20 Feb 2022
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo Krishnakumar Balasubramanian Sinho Chewi Murat A. Erdogdu Adil Salim Matthew Shunshi Zhang 40 60 0 10 Feb 2022
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning Wei Huang Chunrui Liu Yilan Chen Tianyu Liu R. Xu BDL MLT 19 2 0 04 Feb 2022
On Uniform Boundedness Properties of SGD and its Momentum Variants Xiaoyu Wang M. Johansson 23 3 0 25 Jan 2022
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics A. Banerjee Tiancong Chen Xinyan Li Yingxue Zhou 34 8 0 09 Jan 2022
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 30 7 0 02 Jan 2022
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi Ma Zhao-quan Song Guang Lin FedML 27 16 0 09 Dec 2021
Exponential escape efficiency of SGD from sharp minima in non-stationary regime Hikaru Ibayashi Masaaki Imaizumi 26 4 0 07 Nov 2021
Deep Bayesian inference for seismic imaging with tasks Ali Siahkoohi G. Rizzuti Felix J. Herrmann BDL UQCV 38 21 0 10 Oct 2021
Mixing between the Cross Entropy and the Expectation Loss Terms Barak Battash Lior Wolf Tamir Hazan UQCV 20 0 0 12 Sep 2021
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics Alain Durmus Aurélien Enfroy Eric Moulines G. Stoltz 27 17 0 30 Jul 2021
Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information Gholamali Aminian Yuheng Bu Laura Toni M. Rodrigues G. Wornell 30 4 0 28 Jul 2021
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction Dominik Stöger Mahdi Soltanolkotabi ODL 42 75 0 28 Jun 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 35 27 0 21 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 40 52 0 01 Jun 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions Alessandro Barp So Takao M. Betancourt Alexis Arnaudon Mark Girolami 22 17 0 06 May 2021
Manipulating SGD with Data Ordering Attacks Ilia Shumailov Zakhar Shumaylov Dmitry Kazhdan Yiren Zhao Nicolas Papernot Murat A. Erdogdu Ross J. Anderson AAML 112 90 0 19 Apr 2021
Significance tests of feature relevance for a black-box learner Ben Dai Xiaotong Shen Wei Pan 24 25 0 02 Mar 2021
Stochastic Gradient Langevin Dynamics with Variance Reduction Zhishen Huang Stephen Becker 13 7 0 12 Feb 2021
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization Sharu Theresa Jose Osvaldo Simeone G. Durisi 24 17 0 04 Nov 2020
Random Coordinate Underdamped Langevin Monte Carlo Zhiyan Ding Qin Li Jianfeng Lu Stephen J. Wright BDL 29 13 0 22 Oct 2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng Guang Lin F. Liang BDL 42 27 0 19 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 33 8 0 02 Oct 2020
Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence Murat A. Erdogdu Rasa Hosseinzadeh Matthew Shunshi Zhang 86 41 0 22 Jul 2020
On stochastic mirror descent with interacting particles: convergence properties and variance reduction Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 28 12 0 15 Jul 2020
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms A. Lovas Iosif Lytras Miklós Rásonyi Sotirios Sabanis 15 25 0 25 Jun 2020
PAC-Bayes Analysis Beyond the Usual Bounds Omar Rivasplata Ilja Kuzborskij Csaba Szepesvári John Shawe-Taylor 33 80 0 23 Jun 2020
Uncertainty in Gradient Boosting via Ensembles Aleksei Ustimenko Liudmila Prokhorenkova A. Malinin UQCV 28 94 0 18 Jun 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks Umut Simsekli Ozan Sener George Deligiannidis Murat A. Erdogdu 44 55 0 16 Jun 2020
Shape Matters: Understanding the Implicit Bias of the Noise Covariance Jeff Z. HaoChen Colin Wei J. Lee Tengyu Ma 29 93 0 15 Jun 2020
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 35 50 0 14 Jun 2020
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness Murat A. Erdogdu Rasa Hosseinzadeh 6 74 0 27 May 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization Ömer Deniz Akyildiz Sotirios Sabanis 43 17 0 13 Feb 2020
Wasserstein Control of Mirror Langevin Monte Carlo Kelvin Shuangjian Zhang Gabriel Peyré M. Fadili Marcelo Pereyra 19 65 0 11 Feb 2020
Replica Exchange for Non-Convex Optimization Jing-rong Dong Xin T. Tong 14 21 0 23 Jan 2020
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates Jeffrey Negrea Mahdi Haghifam Gintare Karolina Dziugaite Ashish Khisti Daniel M. Roy FedML 113 147 0 06 Nov 2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning Wei Deng Xiao Zhang F. Liang Guang Lin BDL 18 42 0 23 Oct 2019
Non-Gaussianity of Stochastic Gradient Noise A. Panigrahi Raghav Somani Navin Goyal Praneeth Netrapalli 15 52 0 21 Oct 2019
Aggregated Gradient Langevin Dynamics Chao Zhang Jiahao Xie Zebang Shen P. Zhao Tengfei Zhou Hui Qian 28 1 0 21 Oct 2019
Stochastic gradient Markov chain Monte Carlo Christopher Nemeth Paul Fearnhead BDL 22 134 0 16 Jul 2019
SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems Songtao Lu Meisam Razaviyayn Bo Yang Kejun Huang Mingyi Hong 27 11 0 09 Jul 2019
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets Amir-Reza Asadi Emmanuel Abbe BDL AI4CE 34 13 0 26 Jun 2019
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning Huy N. Chau M. Rásonyi 25 10 0 25 Mar 2019
Annealing for Distributed Global Optimization Brian Swenson S. Kar H. Vincent Poor J. M. F. Moura 17 30 0 18 Mar 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 20 22 0 21 Feb 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks Umut Simsekli Levent Sagun Mert Gurbuzbalaban 17 237 0 18 Jan 2019
On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case M. Barkhagen N. H. Chau '. Moulines Miklós Rásonyi S. Sabanis Ying Zhang 21 37 0 06 Dec 2018
Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent Xiaowu Dai Yuhua Zhu 25 11 0 03 Dec 2018
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition Rong Ge Holden Lee Andrej Risteski 14 27 0 29 Nov 2018
Sampling Can Be Faster Than Optimization Yian Ma Yuansi Chen Chi Jin Nicolas Flammarion Michael I. Jordan 11 183 0 20 Nov 2018