Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition

6 March 2015

Furong Huang

Papers citing "Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition"

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Title
$Depth Descent Synchronization in $\mathrm{SO}(D)$$ Depth Descent Synchronization in $\mathrm{SO}(D)$ Tyler Maunu Gilad Lerman MDE 37 2 0 13 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 13 178 0 09 Feb 2020
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization Thomas O'Leary-Roseberry Nick Alger Omar Ghattas ODL 37 9 0 07 Feb 2020
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning Yining Qi Zhihao Qu Song Guo Xin Gao Ruixuan Li Baoliu Ye FedML 18 8 0 22 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity Shiyu Liang Ruoyu Sun R. Srikant 35 19 0 31 Dec 2019
Analysis of the Optimization Landscapes for Overcomplete Representation Learning Qing Qu Yuexiang Zhai Xiao Li Yuqian Zhang Zhihui Zhu 22 9 0 05 Dec 2019
Shadowing Properties of Optimization Algorithms Antonio Orvieto Aurelien Lucchi 33 18 0 12 Nov 2019
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching Anru R. Zhang Yuetian Luo Garvesh Raskutti M. Yuan 27 44 0 09 Nov 2019
Deep neural network Grad-Shafranov solver constrained with measured magnetic signals Semin Joung Jaewook Kim S. Kwak J. Bak S.G. Lee H. Han H.S. Kim Geunho Lee Daeho Kwon Y. Ghim 11 50 0 07 Nov 2019
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Yan Shuo Tan Roman Vershynin 22 35 0 28 Oct 2019
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation Harshat Kumar Alec Koppel Alejandro Ribeiro 104 79 0 18 Oct 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai J. Lee 24 116 0 03 Oct 2019
Quantum algorithm for finding the negative curvature direction in non-convex optimization Kaining Zhang Min-hsiu Hsieh Liu Liu Dacheng Tao 13 3 0 17 Sep 2019
Short-and-Sparse Deconvolution -- A Geometric Approach Yenson Lau Qing Qu Han-Wen Kuo Pengcheng Zhou Yuqian Zhang John N. Wright 19 29 0 28 Aug 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 31 161 0 25 Aug 2019
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization Stefan Vlaski Ali H. Sayed ODL 26 21 0 19 Aug 2019
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise Senwei Liang Zhongzhan Huang Mingfu Liang Haizhao Yang 30 57 0 12 Aug 2019
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift Alekh Agarwal Sham Kakade J. Lee G. Mahajan 13 316 0 01 Aug 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
Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points Stefan Vlaski Ali H. Sayed 27 53 0 03 Jul 2019
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies Kaipeng Zhang Alec Koppel Haoqi Zhu Tamer Basar 44 186 0 19 Jun 2019
Kernel and Rich Regimes in Overparametrized Models Blake E. Woodworth Suriya Gunasekar Pedro H. P. Savarese E. Moroshko Itay Golan J. Lee Daniel Soudry Nathan Srebro 30 352 0 13 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 37 185 0 05 Jun 2019
Stochastic Gradients for Large-Scale Tensor Decomposition T. Kolda David Hong 28 56 0 04 Jun 2019
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems Tianle Cai Ruiqi Gao Jikai Hou Siyu Chen Dong Wang Di He Zhihua Zhang Liwei Wang ODL 21 57 0 28 May 2019
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization Rong Ge Zhize Li Weiyao Wang Xiang Wang 19 33 0 01 May 2019
Annealing for Distributed Global Optimization Brian Swenson S. Kar H. Vincent Poor J. M. F. Moura 25 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
Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent Dmitrii Avdiukhin S. Pupyrev G. Yaroslavtsev 17 18 0 10 Feb 2019
Asymmetric Valleys: Beyond Sharp and Flat Local Minima Haowei He Gao Huang Yang Yuan ODL MLT 28 147 0 02 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 55 961 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 21 94 0 24 Jan 2019
A Deterministic Gradient-Based Approach to Avoid Saddle Points L. Kreusser Stanley J. Osher Bao Wang ODL 32 3 0 21 Jan 2019
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks Mouloud Belbahri Eyyub Sari Sajad Darabi V. Nia MQ 21 1 0 18 Jan 2019
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator Qi Cai Mingyi Hong Yongxin Chen Zhaoran Wang 27 34 0 11 Jan 2019
SGD Converges to Global Minimum in Deep Learning via Star-convex Path Yi Zhou Junjie Yang Huishuai Zhang Yingbin Liang Vahid Tarokh 14 71 0 02 Jan 2019
Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent Xiaowu Dai Yuhua Zhu 25 11 0 03 Dec 2018
Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow Jialin Dong Yuanming Shi Z. Ding 9 59 0 12 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du J. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 44 1,122 0 09 Nov 2018
Subgradient Descent Learns Orthogonal Dictionaries Yu Bai Qijia Jiang Ju Sun 20 51 0 25 Oct 2018
Fault Tolerance in Iterative-Convergent Machine Learning Aurick Qiao Bryon Aragam Bingjing Zhang Eric Xing 26 41 0 17 Oct 2018
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks Sanjeev Arora Nadav Cohen Noah Golowich Wei Hu 27 281 0 04 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 53 1,250 0 04 Oct 2018
Optimal Adaptive and Accelerated Stochastic Gradient Descent Qi Deng Yi Cheng Guanghui Lan 16 8 0 01 Oct 2018
Diffusion Approximations for Online Principal Component Estimation and Global Convergence C. J. Li Mengdi Wang Han Liu Tong Zhang 34 12 0 29 Aug 2018
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property Yi Zhou Zhe Wang Yingbin Liang 24 23 0 22 Aug 2018
Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization G. Wang G. Giannakis Jie Chen MLT 24 131 0 14 Aug 2018
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile P. Mertikopoulos Bruno Lecouat Houssam Zenati Chuan-Sheng Foo V. Chandrasekhar Georgios Piliouras 34 291 0 07 Jul 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 50 570 0 04 Jul 2018