Minimizing Finite Sums with the Stochastic Average Gradient

10 September 2013

Nicolas Le Roux

Papers citing "Minimizing Finite Sums with the Stochastic Average Gradient"

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Title
Direct Acceleration of SAGA using Sampled Negative Momentum Kaiwen Zhou 13 45 0 28 Jun 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou Fanhua Shang James Cheng 22 74 0 28 Jun 2018
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors Atsushi Nitanda Taiji Suzuki 24 10 0 14 Jun 2018
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs Bin Hu S. Wright Laurent Lessard 27 20 0 10 Jun 2018
Towards Riemannian Accelerated Gradient Methods Hongyi Zhang S. Sra 19 53 0 07 Jun 2018
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization Hoi-To Wai Zhuoran Yang Zhaoran Wang Mingyi Hong 30 169 0 03 Jun 2018
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient Tianyi Lin Chenyou Fan Mengdi Wang Michael I. Jordan 22 24 0 01 Jun 2018
Accelerating Incremental Gradient Optimization with Curvature Information Hoi-To Wai Wei Shi César A. Uribe A. Nedić Anna Scaglione 22 12 0 31 May 2018
Stochastic algorithms with descent guarantees for ICA Pierre Ablin Alexandre Gramfort J. Cardoso Francis R. Bach CML 18 7 0 25 May 2018
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication Zebang Shen Aryan Mokhtari Tengfei Zhou P. Zhao Hui Qian 25 56 0 25 May 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen G. Giannakis Tao Sun W. Yin 34 297 0 25 May 2018
Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning Xin Zhang Jia-Wei Liu Zhengyuan Zhu 16 17 0 24 May 2018
Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization Takayuki Kawashima Hironori Fujisawa 14 2 0 21 May 2018
Randomized Smoothing SVRG for Large-scale Nonsmooth Convex Optimization Wenjie Huang 6 0 0 11 May 2018
k-SVRG: Variance Reduction for Large Scale Optimization Anant Raj Sebastian U. Stich 12 6 0 02 May 2018
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations Biyi Fang Diego Klabjan BDL OOD 6 1 0 29 Mar 2018
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization Hoi-To Wai N. Freris A. Nedić Anna Scaglione 12 16 0 22 Mar 2018
D $^2$ : Decentralized Training over Decentralized Data Hanlin Tang Xiangru Lian Ming Yan Ce Zhang Ji Liu 20 348 0 19 Mar 2018
Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate Shen-Yi Zhao Gong-Duo Zhang Ming-Wei Li Wu-Jun Li 19 8 0 15 Mar 2018
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization Andre Milzarek X. Xiao Shicong Cen Zaiwen Wen M. Ulbrich 29 36 0 09 Mar 2018
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning Fanhua Shang Kaiwen Zhou Hongying Liu James Cheng Ivor W. Tsang Lijun Zhang Dacheng Tao L. Jiao 32 65 0 26 Feb 2018
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization Fanhua Shang Yuanyuan Liu Kaiwen Zhou James Cheng K. K. Ng Yuichi Yoshida 27 9 0 26 Feb 2018
An Alternative View: When Does SGD Escape Local Minima? Robert D. Kleinberg Yuanzhi Li Yang Yuan MLT 14 314 0 17 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 13 87 0 15 Feb 2018
A Progressive Batching L-BFGS Method for Machine Learning Raghu Bollapragada Dheevatsa Mudigere J. Nocedal Hao-Jun Michael Shi P. T. P. Tang ODL 21 152 0 15 Feb 2018
Fast Global Convergence via Landscape of Empirical Loss Chao Qu Yan Li Huan Xu 10 0 0 13 Feb 2018
Stochastic quasi-Newton with adaptive step lengths for large-scale problems A. Wills Thomas B. Schon 35 9 0 12 Feb 2018
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization Zeyuan Allen-Zhu ODL 46 52 0 12 Feb 2018
Feature-Distributed SVRG for High-Dimensional Linear Classification Gong-Duo Zhang Shen-Yi Zhao Hao Gao Wu-Jun Li 16 17 0 10 Feb 2018
Semi-Amortized Variational Autoencoders Yoon Kim Sam Wiseman Andrew C. Miller David Sontag Alexander M. Rush BDL DRL 33 243 0 07 Feb 2018
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability Linbo Qiao Tianyi Lin Qi Qin Xicheng Lu 16 1 0 22 Jan 2018
When Does Stochastic Gradient Algorithm Work Well? Lam M. Nguyen Nam H. Nguyen Dzung Phan Jayant Kalagnanam K. Scheinberg 38 15 0 18 Jan 2018
Faster Learning by Reduction of Data Access Time Vinod Kumar Chauhan A. Sharma Kalpana Dahiya 18 5 0 18 Jan 2018
Improved asynchronous parallel optimization analysis for stochastic incremental methods Rémi Leblond Fabian Pedregosa Simon Lacoste-Julien 16 70 0 11 Jan 2018
DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging Senthil Mani A. Sankaran Rahul Aralikatte 34 130 0 04 Jan 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 24 200 0 27 Dec 2017
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice Hongzhou Lin Julien Mairal Zaïd Harchaoui 12 138 0 15 Dec 2017
Mathematics of Deep Learning René Vidal Joan Bruna Raja Giryes Stefano Soatto OOD 30 120 0 13 Dec 2017
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption Stephen Hardy Wilko Henecka Hamish Ivey-Law Richard Nock Giorgio Patrini Guillaume Smith Brian Thorne FedML 21 531 0 29 Nov 2017
Unbiased Simulation for Optimizing Stochastic Function Compositions Jose H. Blanchet D. Goldfarb G. Iyengar Fengpei Li Chao Zhou 23 19 0 20 Nov 2017
Neon2: Finding Local Minima via First-Order Oracles Zeyuan Allen-Zhu Yuanzhi Li 21 130 0 17 Nov 2017
Random gradient extrapolation for distributed and stochastic optimization Guanghui Lan Yi Zhou 15 52 0 15 Nov 2017
Large-Scale Optimal Transport and Mapping Estimation Vivien Seguy B. Damodaran Rémi Flamary Nicolas Courty Antoine Rolet Mathieu Blondel OT 41 243 0 07 Nov 2017
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs Bin Hu Peter M. Seiler Laurent Lessard 24 39 0 03 Nov 2017
Adaptive Sampling Strategies for Stochastic Optimization Raghu Bollapragada R. Byrd J. Nocedal 25 115 0 30 Oct 2017
Linearly convergent stochastic heavy ball method for minimizing generalization error Nicolas Loizou Peter Richtárik 42 45 0 30 Oct 2017
Zeroth Order Nonconvex Multi-Agent Optimization over Networks Davood Hajinezhad Mingyi Hong Alfredo García 21 79 0 27 Oct 2017
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 15 522 0 26 Oct 2017
Duality-free Methods for Stochastic Composition Optimization L. Liu Ji Liu Dacheng Tao 25 16 0 26 Oct 2017
Optimal Rates for Learning with Nyström Stochastic Gradient Methods Junhong Lin Lorenzo Rosasco 16 7 0 21 Oct 2017