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Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation Linda S. L. Tan 108 13 0 01 Sep 2021
Adaptive shot allocation for fast convergence in variational quantum algorithms Andi Gu Angus Lowe Pavel A. Dub Patrick J. Coles A. Arrasmith 67 22 0 23 Aug 2021
Anarchic Federated Learning Haibo Yang Xin Zhang Prashant Khanduri Jia Liu FedML 71 59 0 23 Aug 2021
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks Chenyuan Feng Heng Yang Deshun Hu Zhiwei Zhao Tony Q.S. Quek Geyong Min 103 80 0 20 Aug 2021
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning Anirban Das Timothy Castiglia Shiqiang Wang S. Patterson FedML 104 23 0 19 Aug 2021
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions Arnulf Jentzen Adrian Riekert 82 13 0 10 Aug 2021
On the Hyperparameters in Stochastic Gradient Descent with Momentum Bin Shi 108 14 0 09 Aug 2021
Coordinate descent on the orthogonal group for recurrent neural network training E. Massart V. Abrol 66 11 0 30 Jul 2021
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning Guangfeng Yan Shao-Lun Huang Tian-Shing Lan Linqi Song MQ 47 7 0 30 Jul 2021
Decentralized Federated Learning: Balancing Communication and Computing Costs Wei Liu Li Chen Wenyi Zhang FedML 71 114 0 26 Jul 2021
A general sample complexity analysis of vanilla policy gradient Rui Yuan Robert Mansel Gower A. Lazaric 138 64 0 23 Jul 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 105 13 0 19 Jul 2021
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability Qiyiwen Zhang Zhiqi Bu Kan Chen Qi Long BDL UQCV 72 11 0 18 Jul 2021
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines Shigang Li Torsten Hoefler GNN AI4CE LRM 130 138 0 14 Jul 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 167 83 0 09 Jul 2021
Activated Gradients for Deep Neural Networks Mei Liu Liangming Chen Xiaohao Du Long Jin Mingsheng Shang ODL AI4CE 72 145 0 09 Jul 2021
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity A. Davtyan Sepehr Sameni L. Cerkezi Givi Meishvili Adam Bielski Paolo Favaro ODL 174 3 0 07 Jul 2021
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks J. G. Pauloski Qi Huang Lei Huang Shivaram Venkataraman Kyle Chard Ian Foster Zhao-jie Zhang 86 29 0 04 Jul 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien 94 54 0 30 Jun 2021
Never Go Full Batch (in Stochastic Convex Optimization) I Zaghloul Amir Y. Carmon Tomer Koren Roi Livni 78 14 0 29 Jun 2021
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence Daogao Liu Zhou Lu LRM 39 1 0 28 Jun 2021
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians A. Berahas Frank E. Curtis Michael OÑeill Daniel P. Robinson 76 33 0 24 Jun 2021
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators Zaiwei Chen S. T. Maguluri Sanjay Shakkottai Karthikeyan Shanmugam OffRL 83 13 0 24 Jun 2021
Memory Augmented Optimizers for Deep Learning Paul-Aymeric McRae Prasanna Parthasarathi Mahmoud Assran Sarath Chandar ODL 82 3 0 20 Jun 2021
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning Prashant Khanduri Pranay Sharma Haibo Yang Min-Fong Hong Jia Liu K. Rajawat P. Varshney FedML 59 63 0 19 Jun 2021
Interval and fuzzy physics-informed neural networks for uncertain fields J. Fuhg Ioannis Kalogeris A. Fau N. Bouklas AI4CE 97 19 0 18 Jun 2021
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation Agnieszka Słowik Léon Bottou FaML 77 19 0 17 Jun 2021
A Survey on Fault-tolerance in Distributed Optimization and Machine Learning Shuo Liu AI4CE OOD 79 13 0 16 Jun 2021
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control Amrit Singh Bedi Anjaly Parayil Junyu Zhang Mengdi Wang Alec Koppel 90 15 0 15 Jun 2021
Federated Learning with Buffered Asynchronous Aggregation John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Michael G. Rabbat Mani Malek Dzmitry Huba FedML 99 313 0 11 Jun 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs Vincent Plassier Maxime Vono Alain Durmus Eric Moulines 78 17 0 11 Jun 2021
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems Babak Barazandeh Tianjian Huang George Michailidis 86 12 0 10 Jun 2021
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip Mathieu Even Raphael Berthier Francis R. Bach Nicolas Flammarion Pierre Gaillard Aymeric Dieuleveut Laurent Massoulié Adrien B. Taylor 124 20 0 10 Jun 2021
The dilemma of quantum neural networks Yan Qian Xinbiao Wang Yuxuan Du Xingyao Wu Dacheng Tao 59 31 0 09 Jun 2021
Asynchronous Distributed Optimization with Redundancy in Cost Functions Shuo Liu Nirupam Gupta Nitin H. Vaidya 62 3 0 07 Jun 2021
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models Courtney Paquette Elliot Paquette ODL 102 14 0 07 Jun 2021
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks Chaoyang He Emir Ceyani Keshav Balasubramanian M. Annavaram Salman Avestimehr FedML 73 52 0 04 Jun 2021
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning Blake E. Woodworth Nathan Srebro 81 22 0 04 Jun 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization Valerii Likhosherstov Xingyou Song K. Choromanski Jared Davis Adrian Weller AI4CE 79 5 0 04 Jun 2021
Local Adaptivity in Federated Learning: Convergence and Consistency Jianyu Wang Zheng Xu Zachary Garrett Zachary B. Charles Luyang Liu Gauri Joshi FedML 99 40 0 04 Jun 2021
Improving Neural Network Robustness via Persistency of Excitation Kaustubh Sridhar O. Sokolsky Insup Lee James Weimer AAML 97 20 0 03 Jun 2021
Fine-grained Generalization Analysis of Structured Output Prediction Waleed Mustafa Yunwen Lei Antoine Ledent Marius Kloft 80 9 0 31 May 2021
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective Kushal Chakrabarti Nikhil Chopra ODL AI4CE 83 9 0 31 May 2021
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent Anis Elgabli Chaouki Ben Issaid Amrit Singh Bedi M. Bennis Vaneet Aggarwal FedML 37 4 0 31 May 2021
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations Mingfu Xue Yinghao Wu Zhiyu Wu Yushu Zhang Jian Wang Weiqiang Liu AAML 54 12 0 29 May 2021
A Stochastic Alternating Balance $k$ -Means Algorithm for Fair Clustering Suyun Liu Luis Nunes Vicente 98 11 0 29 May 2021
Online Statistical Inference for Parameters Estimation with Linear-Equality Constraints Ruiqi Liu Mingao Yuan Zuofeng Shang 63 6 0 21 May 2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning Yann Fraboni Richard Vidal Laetitia Kameni Marco Lorenzi FedML 77 196 0 12 May 2021
A Twin Neural Model for Uplift Mouloud Belbahri Olivier Gandouet A. Murua V. Nia CML 13 1 0 11 May 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu Tianyu Ding Jinxin Zhou Xiao Li Chong You Jeremias Sulam Qing Qu 88 204 0 06 May 2021