Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

50 / 1,407 papers shown

Title
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks Jing An Jianfeng Lu 19 4 0 18 Apr 2023
Fast Neural Scene Flow Xueqian Li Jianqiao Zheng Francesco Ferroni J. K. Pontes Simon Lucey 29 27 0 18 Apr 2023
Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load Maximilian Egger Serge Kas Hanna Rawad Bitar FedML 27 0 0 17 Apr 2023
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy Zhenxiao Zhang Yuanxiong Guo Yuguang Fang Yanmin Gong 36 4 0 15 Apr 2023
Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator Haobo Qi Feifei Wang Hansheng Wang 30 13 0 13 Apr 2023
Automatic Gradient Descent: Deep Learning without Hyperparameters Jeremy Bernstein Chris Mingard Kevin Huang Navid Azizan Yisong Yue ODL 16 17 0 11 Apr 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 32 21 0 10 Apr 2023
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance Krishnakumar Balasubramanian Promit Ghosal Ye He 38 5 0 03 Apr 2023
Fast Convergence of Random Reshuffling under Over-Parameterization and the Polyak-Łojasiewicz Condition Chen Fan Christos Thrampoulidis Mark W. Schmidt 33 2 0 02 Apr 2023
Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability Haoyi Xiong Xuhong Li Bo Yu Zhanxing Zhu Dongrui Wu Dejing Dou NoLa 14 0 0 01 Apr 2023
Unified analysis of SGD-type methods Eduard A. Gorbunov 30 2 0 29 Mar 2023
FedAgg: Adaptive Federated Learning with Aggregated Gradients Wenhao Yuan Xuehe Wang FedML 48 0 0 28 Mar 2023
Forget-free Continual Learning with Soft-Winning SubNetworks Haeyong Kang Jaehong Yoon Sultan Rizky Hikmawan Madjid Sung Ju Hwang Chang D. Yoo CLL 36 4 0 27 Mar 2023
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation Ö. Deniz Akyildiz F. R. Crucinio Mark Girolami Tim Johnston Sotirios Sabanis 32 12 0 23 Mar 2023
Make Landscape Flatter in Differentially Private Federated Learning Yi Shi Yingqi Liu Kang Wei Li Shen Xueqian Wang Dacheng Tao FedML 25 54 0 20 Mar 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim Kaiwen Wu Jisu Oh Jacob R. Gardner BDL 31 7 0 18 Mar 2023
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent Rahul Singh A. Shukla Dootika Vats 32 0 0 14 Mar 2023
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond Jaeyoung Cha Jaewook Lee Chulhee Yun 28 23 0 13 Mar 2023
Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication Mengdie Zhang Qi Hu Peng Sun Yonggang Wen Tianwei Zhang GNN 40 5 0 02 Mar 2023
Multi-task neural networks by learned contextual inputs Anders T. Sandnes B. Grimstad O. Kolbjørnsen 14 1 0 01 Mar 2023
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems Yani Feng Keju Tang Xiaoliang Wan Qifeng Liao 19 2 0 01 Mar 2023
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory Tianbo Li Min Lin Zheyuan Hu Kunhao Zheng G. Vignale Kenji Kawaguchi A. Neto K. Novoselov Shuicheng Yan 162 9 0 01 Mar 2023
Maximum Likelihood With a Time Varying Parameter Alberto Lanconelli Christopher S. A. Lauria 16 3 0 28 Feb 2023
Stochastic Gradient Descent under Markovian Sampling Schemes Mathieu Even 19 28 0 28 Feb 2023
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations? Fanny Jourdan Titon Tshiongo Kaninku Nicholas M. Asher Jean-Michel Loubes Laurent Risser FaML 26 4 0 27 Feb 2023
Scalable Neural Network Training over Distributed Graphs Aashish Kolluri Sarthak Choudhary Bryan Hooi Prateek Saxena GNN 21 0 0 25 Feb 2023
Statistical Inference with Stochastic Gradient Methods under $φ$ -mixing Data Ruiqi Liu Xinyu Chen Zuofeng Shang FedML 19 6 0 24 Feb 2023
Why Target Networks Stabilise Temporal Difference Methods Matt Fellows Matthew Smith Shimon Whiteson OOD AAML 21 7 0 24 Feb 2023
Advancements in Federated Learning: Models, Methods, and Privacy Hui Chen Huandong Wang Qingyue Long Depeng Jin Yong Li FedML 44 14 0 22 Feb 2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning Aymeric Dieuleveut G. Fort Eric Moulines Hoi-To Wai 59 12 0 22 Feb 2023
WW-FL: Secure and Private Large-Scale Federated Learning F. Marx T. Schneider Ajith Suresh Tobias Wehrle Christian Weinert Hossein Yalame FedML 25 2 0 20 Feb 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 25 2 0 20 Feb 2023
MaxGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-Noise Ratio for Multi-Task Learning Caoyun Fan Wenqing Chen Jidong Tian Yitian Li Hao He Yaohui Jin 19 2 0 18 Feb 2023
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance Amit Attia Tomer Koren ODL 22 25 0 17 Feb 2023
On the convergence result of the gradient-push algorithm on directed graphs with constant stepsize Woocheol Choi Doheon Kim S. Yun 29 1 0 17 Feb 2023
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization Mathieu Dagréou Thomas Moreau Samuel Vaiter Pierre Ablin 39 12 0 17 Feb 2023
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics Andreas Krämer Aleksander E. P. Durumeric N. Charron Yaoyi Chen C. Clementi Frank Noé AI4CE 30 20 0 14 Feb 2023
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD Matthew Faw Litu Rout C. Caramanis Sanjay Shakkottai 21 37 0 13 Feb 2023
Near-Optimal Non-Convex Stochastic Optimization under Generalized Smoothness Zijian Liu Srikanth Jagabathula Zhengyuan Zhou 24 5 0 13 Feb 2023
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize Mert Gurbuzbalaban Yuanhan Hu Umut Simsekli Lingjiong Zhu LRM 23 1 0 10 Feb 2023
On the Convergence of Stochastic Gradient Descent for Linear Inverse Problems in Banach Spaces Ž. Kereta Bangti Jin 26 6 0 10 Feb 2023
On the Privacy-Robustness-Utility Trilemma in Distributed Learning Youssef Allouah R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 26 21 0 09 Feb 2023
$Extragradient-Type Methods with $\mathcal{O} (1/k)$ Last-Iterate Convergence Rates for Co-Hypomonotone Inclusions$ Extragradient-Type Methods with $\mathcal{O} (1/k)$ Last-Iterate Convergence Rates for Co-Hypomonotone Inclusions Quoc Tran-Dinh 31 2 0 08 Feb 2023
Improving the Model Consistency of Decentralized Federated Learning Yi Shi Li Shen Kang Wei Yan Sun Bo Yuan Xueqian Wang Dacheng Tao FedML 36 51 0 08 Feb 2023
Target-based Surrogates for Stochastic Optimization J. Lavington Sharan Vaswani Reza Babanezhad Mark W. Schmidt Nicolas Le Roux 55 5 0 06 Feb 2023
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity Youssef Allouah Sadegh Farhadkhani R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 45 49 0 03 Feb 2023
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective Chenyu You Weicheng Dai Yifei Min Fenglin Liu David A. Clifton S. Kevin Zhou Lawrence H. Staib James S Duncan 23 69 0 03 Feb 2023
A Survey on Efficient Training of Transformers Bohan Zhuang Jing Liu Zizheng Pan Haoyu He Yuetian Weng Chunhua Shen 31 47 0 02 Feb 2023
Deep networks for system identification: a Survey G. Pillonetto Aleksandr Aravkin Daniel Gedon L. Ljung Antônio H. Ribeiro Thomas B. Schon OOD 37 36 0 30 Jan 2023
Distributed Stochastic Optimization under a General Variance Condition Kun-Yen Huang Xiao Li Shin-Yi Pu FedML 43 6 0 30 Jan 2023