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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
LiD-FL: Towards List-Decodable Federated Learning Hong Liu Liren Shan Han Bao Ronghui You Yuhao Yi Jiancheng Lv FedML 185 0 0 09 Aug 2024
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models Matteo Lapucci Davide Pucci 96 1 0 06 Aug 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 108 0 0 31 Jul 2024
Adaptive Mix for Semi-Supervised Medical Image Segmentation Zhiqiang Shen Peng Cao Junming Su Jinzhu Yang Osmar R. Zaiane 154 0 0 31 Jul 2024
Many Perception Tasks are Highly Redundant Functions of their Input Data Rahul Ramesh Anthony Bisulco Ronald W. DiTullio Linran Wei Vijay Balasubramanian Kostas Daniilidis Pratik Chaudhari 122 2 0 18 Jul 2024
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes Antonio Orvieto Lin Xiao 84 4 0 05 Jul 2024
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 162 0 0 11 Jun 2024
Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization Devyani Maladkar Ruichen Jiang Aryan Mokhtari 104 6 0 07 Jun 2024
Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim Joohwan Ko Yian Ma Jacob R. Gardner 147 2 0 03 Jun 2024
Symmetries in Overparametrized Neural Networks: A Mean-Field View Javier Maass Joaquin Fontbona MLT FedML 186 2 0 30 May 2024
A Pontryagin Perspective on Reinforcement Learning Onno Eberhard Claire Vernade Michael Muehlebach 122 3 0 28 May 2024
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks Felix Dangel Johannes Müller Marius Zeinhofer ODL 107 11 0 24 May 2024
Almost sure convergence rates of stochastic gradient methods under gradient domination Simon Weissmann Sara Klein Waïss Azizian Leif Döring 93 3 0 22 May 2024
Reinforcement learning Florentin Wörgötter 97 2,526 0 16 May 2024
A Full Adagrad algorithm with O(Nd) operations Antoine Godichon-Baggioni Wei Lu Bruno Portier ODL 136 0 0 03 May 2024
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients Sachin Garg A. Berahas Michal Dereziñski 92 1 0 23 Apr 2024
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization Yaqun Yang Jinlong Lei 70 0 0 21 Apr 2024
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series Zahra Zamanzadeh Darban Yiyuan Yang Geoffrey I. Webb Charu C. Aggarwal Qingsong Wen Xiaojun Jia Mahsa Salehi 127 1 0 17 Apr 2024
I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey Noah Lewis J. L. Bez Suren Byna 113 0 0 16 Apr 2024
Stochastic Online Optimization for Cyber-Physical and Robotic Systems Hao Ma Melanie Zeilinger Michael Muehlebach 92 1 0 08 Apr 2024
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance Qi Zhang Yi Zhou Shaofeng Zou 140 7 0 01 Apr 2024
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks Yongyi Yang Jiaming Yang Wei Hu Michal Dereziñski 90 0 0 26 Mar 2024
AI and Memory Wall A. Gholami Z. Yao Sehoon Kim Coleman Hooper Michael W. Mahoney Kurt Keutzer 84 161 0 21 Mar 2024
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms Toki Tahmid Inan Mingrui Liu Amarda Shehu 63 0 0 01 Mar 2024
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models Amit Dhurandhar Tejaswini Pedapati Ronny Luss Soham Dan Aurélie C. Lozano Payel Das Georgios Kollias 84 3 0 28 Feb 2024
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations Yao Shu Jiongfeng Fang Y. He Fei Richard Yu 66 0 0 18 Feb 2024
Switch EMA: A Free Lunch for Better Flatness and Sharpness Siyuan Li Zicheng Liu Juanxi Tian Ge Wang Zedong Wang ... Cheng Tan Tao Lin Yang Liu Baigui Sun Stan Z. Li 66 6 0 14 Feb 2024
Scalable Kernel Logistic Regression with Nyström Approximation: Theoretical Analysis and Application to Discrete Choice Modelling José Ángel Martín-Baos Ricardo García-Ródenas Luis Rodriguez-Benitez Michel Bierlaire 50 1 0 09 Feb 2024
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization Ling Liang Zusen Xu Kim-Chuan Toh Jia Jie Zhu 152 4 0 08 Feb 2024
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions Yusu Hong Junhong Lin 129 13 0 06 Feb 2024
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation Sobihan Surendran Antoine Godichon-Baggioni Adeline Fermanian Sylvain Le Corff 109 2 0 05 Feb 2024
Emergence of heavy tails in homogenized stochastic gradient descent Zhe Jiao Martin Keller-Ressel 61 1 0 02 Feb 2024
Towards Quantum-Safe Federated Learning via Homomorphic Encryption: Learning with Gradients Guangfeng Yan Shanxiang Lyu Hanxu Hou Zhiyong Zheng Linqi Song FedML 20 1 0 02 Feb 2024
Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods Xiliang Yang Yifei Xiong Zhijian He 84 0 0 30 Jan 2024
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications Rajeeva Laxman Karandikar M. Vidyasagar 66 10 0 05 Dec 2023
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations Antoine Godichon-Baggioni Nicklas Werge ODL 118 4 0 29 Nov 2023
SensLI: Sensitivity-Based Layer Insertion for Neural Networks Evelyn Herberg Roland A. Herzog Frederik Köhne Leonie Kreis Anton Schiela 50 0 0 27 Nov 2023
Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia Milad Baghalzadeh Shishehgarkhaneh R. Moehler Yihai Fang Amer A. Hijazi Hamed Aboutorab 100 10 0 23 Nov 2023
High Probability Guarantees for Random Reshuffling Hengxu Yu Xiao Li 91 2 0 20 Nov 2023
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization Philipp Dahlinger P. Becker Maximilian Hüttenrauch Gerhard Neumann 46 0 0 31 Oct 2023
Optimization of utility-based shortfall risk: A non-asymptotic viewpoint Sumedh Gupte A. PrashanthL. Sanjay P. Bhat 54 1 0 28 Oct 2023
Performative Prediction: Past and Future Moritz Hardt Celestine Mendler-Dünner 159 26 0 25 Oct 2023
Graph Neural Networks and Applied Linear Algebra Nicholas S. Moore Eric C. Cyr Peter Ohm C. Siefert R. Tuminaro 75 4 0 21 Oct 2023
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset Weijie Liu Xiaoxi Zhang Jingpu Duan Carlee Joe-Wong Zhi Zhou Xu Chen 80 9 0 20 Oct 2023
High Throughput Training of Deep Surrogates from Large Ensemble Runs Lucas Meyer M. Schouler R. Caulk Alejandro Ribés Bruno Raffin AI4CE 57 6 0 28 Sep 2023
A Novel Gradient Methodology with Economical Objective Function Evaluations for Data Science Applications Christian Varner Vivak Patel 71 2 0 19 Sep 2023
Derivation of Coordinate Descent Algorithms from Optimal Control Theory I. Michael Ross 27 1 0 07 Sep 2023
We Don't Need No Adam, All We Need Is EVE: On The Variance of Dual Learning Rate And Beyond A. Khadangi ODL 109 0 0 21 Aug 2023
Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent Xiaoge Deng Li Shen Shengwei Li Tao Sun Dongsheng Li Dacheng Tao 85 3 0 18 Aug 2023
Quantile Optimization via Multiple Timescale Local Search for Black-box Functions Jiaqiao Hu Meichen Song Michael Fu 28 6 0 15 Aug 2023