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
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 35 101 0 02 Mar 2022
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum F. Malandrino C. Chiasserini G. di Giacomo 14 7 0 23 Feb 2022
Asynchronous Fully-Decentralized SGD in the Cluster-Based Model Hagit Attiya N. Schiller FedML 28 0 0 22 Feb 2022
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness Beomsu Kim Junghoon Seo AAML 28 0 0 21 Feb 2022
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate Aixiang Chen Chen Jinting Zhang Zanbo Zhang Zhihong Li 48 0 0 21 Feb 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 31 8 0 18 Feb 2022
Temporal Difference Learning with Continuous Time and State in the Stochastic Setting Ziad Kobeissi Francis R. Bach OffRL 21 3 0 16 Feb 2022
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Rui Hu Yanmin Gong Yuanxiong Guo FedML 27 65 0 15 Feb 2022
Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations Ziheng Wang Justin A. Sirignano 38 2 0 14 Feb 2022
Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems L. Nurbekyan Wanzhou Lei Yunbo Yang 15 12 0 13 Feb 2022
Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schrödinger's Equation Sangeeta Srivastava Samuel W. Olin V. Podolskiy Anuj Karpatne Wei‐Cheng Lee Anish Arora 29 0 0 12 Feb 2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance Matthew Faw Isidoros Tziotis C. Caramanis Aryan Mokhtari Sanjay Shakkottai Rachel A. Ward 29 60 0 11 Feb 2022
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Yujia Jin Aaron Sidford Kevin Tian 21 30 0 09 Feb 2022
Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysis S. Perlaza Gaetan Bisson I. Esnaola A. Jean-Marie Stefano Rini 12 13 0 09 Feb 2022
On Almost Sure Convergence Rates of Stochastic Gradient Methods Jun Liu Ye Yuan 26 36 0 09 Feb 2022
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization Trang H. Tran K. Scheinberg Lam M. Nguyen 40 11 0 07 Feb 2022
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution Lam M. Nguyen Trang H. Tran Marten van Dijk 41 3 0 07 Feb 2022
Variance reduced stochastic optimization over directed graphs with row and column stochastic weights Muhammad I. Qureshi Ran Xin S. Kar U. Khan 26 3 0 07 Feb 2022
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods Amirkeivan Mohtashami Sebastian U. Stich Martin Jaggi 26 13 0 03 Feb 2022
When Do Flat Minima Optimizers Work? Jean Kaddour Linqing Liu Ricardo M. A. Silva Matt J. Kusner ODL 28 58 0 01 Feb 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 26 3 0 31 Jan 2022
A subsampling approach for Bayesian model selection Jon Lachmann G. Storvik F. Frommlet Aliaksadr Hubin BDL 27 2 0 31 Jan 2022
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning Xing Xu Rongpeng Li Zhifeng Zhao Honggang Zhang FedML 31 6 0 30 Jan 2022
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization G. Luca E. Silverstein 48 10 0 26 Jan 2022
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives Pierre-Alexandre Mattei J. Frellsen 29 4 0 26 Jan 2022
Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network Peng Shi Zhi Zeng Tianshou Liang AI4CE 31 20 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 78 57 0 24 Jan 2022
Learning to Minimize the Remainder in Supervised Learning Yan Luo Yongkang Wong Mohan S. Kankanhalli Qi Zhao 57 1 0 23 Jan 2022
Optimal variance-reduced stochastic approximation in Banach spaces Wenlong Mou K. Khamaru Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 39 8 0 21 Jan 2022
Near-Optimal Sparse Allreduce for Distributed Deep Learning Shigang Li Torsten Hoefler 31 51 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 25 25 0 16 Jan 2022
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer Vinayak Gupta Srikanta J. Bedathur 47 19 0 16 Jan 2022
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach T. Wan L. Hong 24 10 0 15 Jan 2022
Path differentiability of ODE flows S. Marx Edouard Pauwels 17 2 0 11 Jan 2022
Federated Optimization of Smooth Loss Functions Ali Jadbabaie A. Makur Devavrat Shah FedML 32 7 0 06 Jan 2022
$Asymptotics of $\ell_2$ Regularized Network Embeddings$ Asymptotics of $\ell_2$ Regularized Network Embeddings A. Davison 33 0 0 05 Jan 2022
Stochastic regularized majorization-minimization with weakly convex and multi-convex surrogates Hanbaek Lyu 18 3 0 05 Jan 2022
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics Enea Monzio Compagnoni Anna Scampicchio Luca Biggio Antonio Orvieto Thomas Hofmann Josef Teichmann 35 11 0 02 Jan 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 39 10 0 28 Dec 2021
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 32 107 0 28 Dec 2021
AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent Nhuong V. Nguyen Song Han 21 2 0 27 Dec 2021
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) Tengchan Zeng Omid Semiari Walid Saad M. Bennis 39 3 0 26 Dec 2021
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation Xingyu Li Zhe Qu Bo Tang Zhuo Lu FedML 38 26 0 22 Dec 2021
Simple and Effective Balance of Contrastive Losses Arnaud Sors Rafael Sampaio de Rezende Sarah Ibrahimi J. Andreoli SSL 30 1 0 22 Dec 2021
Improving Robustness with Image Filtering M. Terzi Mattia Carletti Gian Antonio Susto AAML 31 0 0 21 Dec 2021
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity Yuchang Sun Jiawei Shao Yuyi Mao Jessie Hui Wang Jun Zhang FedML 26 31 0 20 Dec 2021
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats Brian Chmiel Ron Banner Elad Hoffer Hilla Ben Yaacov Daniel Soudry MQ 33 23 0 19 Dec 2021
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey Jiaoyan Chen Yuxia Geng Zhuo Chen Jeff Z. Pan Yuan He Wen Zhang Ian Horrocks Hua-zeng Chen 30 43 0 18 Dec 2021
SCORE: Approximating Curvature Information under Self-Concordant Regularization Adeyemi Damilare Adeoye Alberto Bemporad 20 4 0 14 Dec 2021
Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization Hideaki Iiduka 13 0 0 14 Dec 2021