Optimization Methods for Large-Scale Machine Learning

15 June 2016

Papers citing "Optimization Methods for Large-Scale Machine Learning"

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Title
Almost Tune-Free Variance Reduction Bingcong Li Lingda Wang G. Giannakis 6 17 0 25 Aug 2019
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization Laurent Risser Alberto González Sanz Quentin Vincenot Jean-Michel Loubes 35 21 0 15 Aug 2019
A review on Deep Reinforcement Learning for Fluid Mechanics Paul Garnier J. Viquerat Jean Rabault A. Larcher A. Kuhnle E. Hachem AI4CE 29 254 0 12 Aug 2019
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Chen Wang Qin Yu Kai Zhou D. Hui Xiaofeng Gong Ruisen Luo 29 22 0 09 Aug 2019
Bias of Homotopic Gradient Descent for the Hinge Loss Denali Molitor Deanna Needell Rachel A. Ward 14 5 0 26 Jul 2019
Interactive Lungs Auscultation with Reinforcement Learning Agent Tomasz Grzywalski Riccardo Belluzzo S. Drgas A. Cwalinska Honorata Hafke-Dys LM&MA 18 3 0 25 Jul 2019
Learning the Tangent Space of Dynamical Instabilities from Data Antoine Blanchard T. Sapsis 18 8 0 24 Jul 2019
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions Matthew Faw Rajat Sen Karthikeyan Shanmugam Constantine Caramanis Sanjay Shakkottai 41 3 0 23 Jul 2019
An introduction to decentralized stochastic optimization with gradient tracking Ran Xin S. Kar U. Khan 36 10 0 23 Jul 2019
Bilevel Optimization, Deep Learning and Fractional Laplacian Regularization with Applications in Tomography Harbir Antil Z. Di R. Khatri 27 49 0 22 Jul 2019
Speeding Up Iterative Closest Point Using Stochastic Gradient Descent F. A. Maken F. Ramos Lionel Ott 3DPC 22 13 0 22 Jul 2019
Adaptive Weight Decay for Deep Neural Networks Kensuke Nakamura Byung-Woo Hong 24 42 0 21 Jul 2019
Techniques for Automated Machine Learning Yi-Wei Chen Qingquan Song Xia Hu 23 48 0 21 Jul 2019
An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks S. I. Valdez A. R. Domínguez ODL 24 1 0 12 Jul 2019
Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman Equations Tenavi Nakamura-Zimmerer Q. Gong W. Kang 29 132 0 11 Jul 2019
Spatiotemporal Local Propagation Alessandro Betti Marco Gori 13 1 0 11 Jul 2019
The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning Suyun Liu Luis Nunes Vicente 27 72 0 10 Jul 2019
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization Kenji Kawaguchi Haihao Lu ODL 35 62 0 09 Jul 2019
Unified Optimal Analysis of the (Stochastic) Gradient Method Sebastian U. Stich 29 112 0 09 Jul 2019
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets Reuben Dorent Wenqi Li J. Ekanayake Sebastien Ourselin Tom Vercauteren 28 4 0 07 Jul 2019
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs B. Grimstad H. Andersson 27 139 0 06 Jul 2019
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning Zheng Fang Adrian S. Wong Kangbo Hao Alexander J. A. Ty H. Abarbanel 19 1 0 06 Jul 2019
Variance Reduction for Matrix Games Y. Carmon Yujia Jin Aaron Sidford Kevin Tian 27 63 0 03 Jul 2019
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses Ulysse Marteau-Ferey Francis R. Bach Alessandro Rudi 22 36 0 03 Jul 2019
The Role of Memory in Stochastic Optimization Antonio Orvieto Jonas Köhler Aurelien Lucchi 27 30 0 02 Jul 2019
Network-accelerated Distributed Machine Learning Using MLFabric Raajay Viswanathan Aditya Akella AI4CE 21 4 0 30 Jun 2019
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization Seonho Park Seung Hyun Jung P. Pardalos ODL 34 15 0 27 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 45 122 0 23 Jun 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 30 3 0 22 Jun 2019
Fully Decoupled Neural Network Learning Using Delayed Gradients Huiping Zhuang Yi Wang Qinglai Liu Shuai Zhang Zhiping Lin FedML 30 30 0 21 Jun 2019
Accelerating Mini-batch SARAH by Step Size Rules Zhuang Yang Zengping Chen Cheng-Yu Wang 27 15 0 20 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective Shiliang Sun Zehui Cao Han Zhu Jing Zhao 22 552 0 17 Jun 2019
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning N. Skatchkovsky Osvaldo Simeone 13 17 0 11 Jun 2019
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training Paul Grigas Alfonso Lobos Nathan Vermeersch 32 5 0 09 Jun 2019
Practical Deep Learning with Bayesian Principles Kazuki Osawa S. Swaroop Anirudh Jain Runa Eschenhagen Richard Turner Rio Yokota Mohammad Emtiyaz Khan BDL UQCV 56 242 0 06 Jun 2019
Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networks Yi Ren D. Goldfarb 27 35 0 05 Jun 2019
On the Convergence of SARAH and Beyond Bingcong Li Meng Ma G. Giannakis 41 26 0 05 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan Alexander Immer Ehsan Abedi M. Korzepa UQCV BDL 47 122 0 05 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis Cynthia Rudin David Carlson HAI 32 34 0 04 Jun 2019
A Generic Acceleration Framework for Stochastic Composite Optimization A. Kulunchakov Julien Mairal 32 43 0 03 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1 Majid Jahani M. Nazari S. Rusakov A. Berahas Martin Takávc 28 14 0 30 May 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent Frederik Kunstner Lukas Balles Philipp Hennig 40 210 0 29 May 2019
An Inertial Newton Algorithm for Deep Learning Camille Castera Jérôme Bolte Cédric Févotte Edouard Pauwels PINN ODL 36 62 0 29 May 2019
Where is the Information in a Deep Neural Network? Alessandro Achille Giovanni Paolini Stefano Soatto 26 81 0 29 May 2019
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization Yifan Hu Xin Chen Niao He 22 35 0 28 May 2019
Recursive Estimation for Sparse Gaussian Process Regression Manuel Schürch Dario Azzimonti A. Benavoli Marco Zaffalon 38 32 0 28 May 2019
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning Zaiwei Chen Sheng Zhang Thinh T. Doan John-Paul Clarke S. T. Maguluri 38 58 0 27 May 2019
Robustness of accelerated first-order algorithms for strongly convex optimization problems Hesameddin Mohammadi Meisam Razaviyayn M. Jovanović 17 41 0 27 May 2019
Decentralized Bayesian Learning over Graphs Anusha Lalitha Xinghan Wang O. Kilinc Y. Lu T. Javidi F. Koushanfar FedML 33 25 0 24 May 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension Yunfei Teng Wenbo Gao F. Chalus A. Choromańska D. Goldfarb Adrian Weller 37 12 0 24 May 2019