Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
Adversarial Attacks on Optimization based Planners Sai H. Vemprala Ashish Kapoor AAML 36 12 0 30 Oct 2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Quoc Tran-Dinh Phuong Ha Nguyen FedML 44 10 0 27 Oct 2020
Optimal Client Sampling for Federated Learning Wenlin Chen Samuel Horváth Peter Richtárik FedML 47 192 0 26 Oct 2020
Byzantine Resilient Distributed Multi-Task Learning Jiani Li W. Abbas X. Koutsoukos 22 8 0 25 Oct 2020
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD Jiayi Wang Shiqiang Wang Rong-Rong Chen Mingyue Ji FedML 36 51 0 24 Oct 2020
Sample Efficient Reinforcement Learning with REINFORCE Junzi Zhang Jongho Kim Brendan O'Donoghue Stephen P. Boyd 44 101 0 22 Oct 2020
How Data Augmentation affects Optimization for Linear Regression Boris Hanin Yi Sun 12 16 0 21 Oct 2020
Progressive Batching for Efficient Non-linear Least Squares Huu Le Christopher Zach E. Rosten Oliver J. Woodford 14 3 0 21 Oct 2020
Dual Averaging is Surprisingly Effective for Deep Learning Optimization Samy Jelassi Aaron Defazio 38 4 0 20 Oct 2020
On the Difficulty of Unbiased Alpha Divergence Minimization Tomas Geffner Justin Domke 65 18 0 19 Oct 2020
Factorization Machines with Regularization for Sparse Feature Interactions Kyohei Atarashi S. Oyama M. Kurihara 19 5 0 19 Oct 2020
Bi-level Score Matching for Learning Energy-based Latent Variable Models Fan Bao Chongxuan Li Kun Xu Hang Su Jun Zhu Bo Zhang 33 13 0 15 Oct 2020
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers Zheng Chai Yujing Chen Ali Anwar Liang Zhao Yue Cheng Huzefa Rangwala FedML 26 122 0 12 Oct 2020
AEGD: Adaptive Gradient Descent with Energy Hailiang Liu Xuping Tian ODL 27 11 0 10 Oct 2020
A variable metric mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize Tengteng Yu Xinwei Liu Yuhong Dai Jie Sun 26 4 0 02 Oct 2020
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning Xinyue Liang Alireza M. Javid Mikael Skoglund S. Chatterjee FedML 25 4 0 29 Sep 2020
Normalization Techniques in Training DNNs: Methodology, Analysis and Application Lei Huang Jie Qin Yi Zhou Fan Zhu Li Liu Ling Shao AI4CE 12 255 0 27 Sep 2020
An optimization problem for continuous submodular functions L. Csirmaz 23 2 0 26 Sep 2020
Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold G. Chirco Luigi Malagò Giovanni Pistone 19 4 0 20 Sep 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 44 79 0 17 Sep 2020
A general framework for decentralized optimization with first-order methods Ran Xin Shi Pu Angelia Nedić U. Khan 25 87 0 12 Sep 2020
A Markov Decision Process Approach to Active Meta Learning Bingjia Wang Alec Koppel Vikram Krishnamurthy 6 1 0 10 Sep 2020
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets Reuben Dorent Thomas C Booth Wenqi Li Carole H. Sudre S. Kafiabadi M. Jorge Cardoso Sebastien Ourselin Tom Vercauteren 26 25 0 08 Sep 2020
On Communication Compression for Distributed Optimization on Heterogeneous Data Sebastian U. Stich 53 23 0 04 Sep 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 29 179 0 01 Sep 2020
Beyond variance reduction: Understanding the true impact of baselines on policy optimization Wesley Chung Valentin Thomas Marlos C. Machado Nicolas Le Roux OffRL 24 22 0 31 Aug 2020
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach Malena Reiners K. Klamroth Michael Stiglmayr 22 16 0 31 Aug 2020
Wireless for Machine Learning Henrik Hellström J. M. B. D. Silva Mohammad Mohammadi Amiri Mingzhe Chen Viktoria Fodor H. Vincent Poor Carlo Fischione 22 18 0 31 Aug 2020
Understanding and Detecting Convergence for Stochastic Gradient Descent with Momentum Jerry Chee Ping Li 18 11 0 27 Aug 2020
Optimization with learning-informed differential equation constraints and its applications Guozhi Dong M. Hintermueller Kostas Papafitsoros PINN 37 14 0 25 Aug 2020
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization Tianyi Chen Yuejiao Sun W. Yin 52 81 0 25 Aug 2020
Channel-Directed Gradients for Optimization of Convolutional Neural Networks Dong Lao Peihao Zhu Peter Wonka G. Sundaramoorthi 42 3 0 25 Aug 2020
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning Steven L. Brunton J. Nathan Kutz Krithika Manohar Aleksandr Aravkin K. Morgansen ... J. Buttrick Jeffrey Poskin Agnes Blom-Schieber Thomas Hogan Darren McDonald AI4CE 31 123 0 24 Aug 2020
Improving predictions of Bayesian neural nets via local linearization Alexander Immer M. Korzepa Matthias Bauer BDL 22 11 0 19 Aug 2020
Whitening and second order optimization both make information in the dataset unusable during training, and can reduce or prevent generalization Neha S. Wadia Daniel Duckworth S. Schoenholz Ethan Dyer Jascha Narain Sohl-Dickstein 34 13 0 17 Aug 2020
Fast decentralized non-convex finite-sum optimization with recursive variance reduction Ran Xin U. Khan S. Kar 29 43 0 17 Aug 2020
Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems P. Safari B. Shariati J. Fischer FedML 17 6 0 17 Aug 2020
Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs Muhammad I. Qureshi Ran Xin S. Kar U. Khan 32 20 0 13 Aug 2020
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE) Nirupam Gupta Shuo Liu Nitin H. Vaidya FedML 32 11 0 11 Aug 2020
An improved convergence analysis for decentralized online stochastic non-convex optimization Ran Xin U. Khan S. Kar 44 100 0 10 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 25 110 0 10 Aug 2020
DINE: A Framework for Deep Incomplete Network Embedding Ke Hou Jiaying Liu Yin Peng Bo Xu Ivan Lee Feng Xia 29 3 0 09 Aug 2020
Large-time asymptotics in deep learning Carlos Esteve Borjan Geshkovski Dario Pighin Enrique Zuazua 18 34 0 06 Aug 2020
On the Convergence of SGD with Biased Gradients Ahmad Ajalloeian Sebastian U. Stich 6 84 0 31 Jul 2020
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization Vasco Lopes L. A. Alexandre BDL 11 0 0 31 Jul 2020
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks Jun Shu Yanwen Zhu Qian Zhao Zongben Xu Deyu Meng 23 7 0 29 Jul 2020
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems Wentai Wu Ligang He Weiwei Lin Rui Mao 25 78 0 28 Jul 2020
A Comparison of Optimization Algorithms for Deep Learning Derya Soydaner 87 151 0 28 Jul 2020
Multi-Level Local SGD for Heterogeneous Hierarchical Networks Timothy Castiglia Anirban Das S. Patterson 26 13 0 27 Jul 2020
Binary Search and First Order Gradient Based Method for Stochastic Optimization V. Pandey ODL 11 0 0 27 Jul 2020