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Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

50 / 866 papers shown

Title
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 95 8 0 13 Jun 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 70 2 0 13 Jun 2022
A Unified Convergence Theorem for Stochastic Optimization Methods Xiao Li Andre Milzarek 91 12 0 08 Jun 2022
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks Zhibin Wang Yong Zhou Yuanming Shi W. Zhuang 87 72 0 06 Jun 2022
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning Kushal Chakrabarti Nikhil Chopra ODL AI4CE 140 6 0 04 Jun 2022
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees Jue Wang Binhang Yuan Luka Rimanic Yongjun He Tri Dao Beidi Chen Christopher Ré Ce Zhang AI4CE 110 13 0 02 Jun 2022
Computing the Variance of Shuffling Stochastic Gradient Algorithms via Power Spectral Density Analysis Carles Domingo-Enrich 47 0 0 01 Jun 2022
Online Deep Equilibrium Learning for Regularization by Denoising Jiaming Liu Xiaojian Xu Weijie Gan Shirin Shoushtari Ulugbek S. Kamilov 114 28 0 25 May 2022
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization Benjamin Dubois-Taine Francis R. Bach Quentin Berthet Adrien B. Taylor 94 5 0 25 May 2022
Learning from time-dependent streaming data with online stochastic algorithms Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 122 3 0 25 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 73 4 0 15 May 2022
Decentralized Stochastic Optimization with Inherent Privacy Protection Yongqiang Wang H. Vincent Poor 99 39 0 08 May 2022
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks Zeyi Tao Jindi Wu Qi Xia Qun Li 60 9 0 05 May 2022
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey Djamila Bouhata Hamouma Moumen Moumen Hamouma Ahcène Bounceur AI4CE 106 8 0 05 May 2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning Samuel Horváth Maziar Sanjabi Lin Xiao Peter Richtárik Michael G. Rabbat FedML 88 21 0 27 Apr 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 118 17 0 20 Apr 2022
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning Afsaneh Mahmoudi H. S. Ghadikolaei José Hélio da Cruz Júnior Carlo Fischione 71 9 0 16 Apr 2022
Minimizing Control for Credit Assignment with Strong Feedback Alexander Meulemans Matilde Tristany Farinha Maria R. Cervera João Sacramento Benjamin Grewe 76 17 0 14 Apr 2022
Rethinking Exponential Averaging of the Fisher C. Puiu 54 1 0 10 Apr 2022
Distributed Evolution Strategies for Black-box Stochastic Optimization Xiaoyu He Zibin Zheng Chuan Chen Yuren Zhou Chuan Luo Qingwei Lin 56 5 0 09 Apr 2022
Federated Learning with Partial Model Personalization Krishna Pillutla Kshitiz Malik Abdel-rahman Mohamed Michael G. Rabbat Maziar Sanjabi Lin Xiao FedML 105 170 0 08 Apr 2022
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency Zhiwu Qing Shiwei Zhang Ziyuan Huang Yi Tian Xu Xiang Wang Mingqian Tang Changxin Gao Rong Jin Nong Sang SSL AI4TS 76 17 0 06 Apr 2022
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise D. Jakovetić Dragana Bajović Anit Kumar Sahu S. Kar Nemanja Milošević Dusan Stamenkovic 62 14 0 06 Apr 2022
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography Junhyung Lyle Kim Taha Toghani César A. Uribe Anastasios Kyrillidis 56 3 0 22 Mar 2022
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients Brian Chmiel Itay Hubara Ron Banner Daniel Soudry 95 9 0 21 Mar 2022
A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima Tae-Eon Ko Xiantao Li 72 2 0 21 Mar 2022
Convergence rates of the stochastic alternating algorithm for bi-objective optimization Suyun Liu Luis Nunes Vicente 58 3 0 20 Mar 2022
On the Properties of Adversarially-Trained CNNs Mattia Carletti M. Terzi Gian Antonio Susto AAML 64 1 0 17 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 114 49 0 09 Mar 2022
On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features Jinxin Zhou Xiao Li Tian Ding Chong You Qing Qu Zhihui Zhu 78 102 0 02 Mar 2022
Asynchronous Fully-Decentralized SGD in the Cluster-Based Model Hagit Attiya N. Schiller FedML 91 0 0 22 Feb 2022
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate Aixiang Chen Chen Jinting Zhang Zanbo Zhang Zhihong Li 67 0 0 21 Feb 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 91 8 0 18 Feb 2022
Temporal Difference Learning with Continuous Time and State in the Stochastic Setting Ziad Kobeissi Francis R. Bach OffRL 99 4 0 16 Feb 2022
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Rui Hu Yanmin Gong Yuanxiong Guo FedML 87 73 0 15 Feb 2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance Matthew Faw Isidoros Tziotis Constantine Caramanis Aryan Mokhtari Sanjay Shakkottai Rachel A. Ward 88 61 0 11 Feb 2022
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Yujia Jin Aaron Sidford Kevin Tian 87 31 0 09 Feb 2022
On Almost Sure Convergence Rates of Stochastic Gradient Methods Jun Liu Ye Yuan 97 38 0 09 Feb 2022
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods Amirkeivan Mohtashami Sebastian U. Stich Martin Jaggi 71 13 0 03 Feb 2022
When Do Flat Minima Optimizers Work? Jean Kaddour Linqing Liu Ricardo M. A. Silva Matt J. Kusner ODL 142 65 0 01 Feb 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 86 3 0 31 Jan 2022
A subsampling approach for Bayesian model selection Jon Lachmann G. Storvik F. Frommlet Aliaksadr Hubin BDL 53 2 0 31 Jan 2022
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning Xing Xu Rongpeng Li Zhifeng Zhao Honggang Zhang FedML 64 6 0 30 Jan 2022
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization G. Luca E. Silverstein 85 11 0 26 Jan 2022
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives Pierre-Alexandre Mattei J. Frellsen 56 4 0 26 Jan 2022
Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network Peng Shi Zhi Zeng Tianshou Liang AI4CE 60 23 0 26 Jan 2022
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning Wenzhi Fang Ziyi Yu Yuning Jiang Yuanming Shi Colin N. Jones Yong Zhou FedML 128 60 0 24 Jan 2022
Optimal variance-reduced stochastic approximation in Banach spaces Wenlong Mou K. Khamaru Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 85 9 0 21 Jan 2022
Near-Optimal Sparse Allreduce for Distributed Deep Learning Shigang Li Torsten Hoefler 62 53 0 19 Jan 2022
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent R. Laumont Valentin De Bortoli Andrés Almansa J. Delon Alain Durmus Marcelo Pereyra 67 26 0 16 Jan 2022