Fast Decentralized Federated Low Rank Matrix Recovery from Column-wise Linear Projections
暂无分享,去创建一个
[1] Shana Moothedath,et al. Comparing Decentralized Gradient Descent Approaches and Guarantees , 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[2] Shana Moothedath,et al. Dec-AltProjGDmin: Fully-Decentralized Alternating Projected Gradient Descent for Low Rank Column-wise Compressive Sensing , 2022, 2022 IEEE 61st Conference on Decision and Control (CDC).
[3] Namrata Vaswani,et al. Fast Low Rank column-wise Compressive Sensing , 2022, 2022 IEEE International Symposium on Information Theory (ISIT).
[4] Namrata Vaswani,et al. Fully Decentralized and Federated Low Rank Compressive Sensing , 2021, 2022 American Control Conference (ACC).
[5] U. Khan,et al. A Fast Randomized Incremental Gradient Method for Decentralized Nonconvex Optimization , 2020, IEEE Transactions on Automatic Control.
[6] Ashkan Panahi,et al. Decentralized Constrained Optimization: Double Averaging and Gradient Projection , 2021, 2021 60th IEEE Conference on Decision and Control (CDC).
[7] W. Bajwa,et al. Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation , 2021, IEEE Transactions on Signal and Information Processing over Networks.
[8] Haishan Ye,et al. DeEPCA: Decentralized Exact PCA with Linear Convergence Rate , 2021, J. Mach. Learn. Res..
[9] Jianqing Fan,et al. Spectral Methods for Data Science: A Statistical Perspective , 2020, Found. Trends Mach. Learn..
[10] Seyedehsara Nayer,et al. Sample-Efficient Low Rank Phase Retrieval , 2020, IEEE Transactions on Information Theory.
[11] O. Papaspiliopoulos. High-Dimensional Probability: An Introduction with Applications in Data Science , 2020 .
[12] Shuang Li,et al. The Global Geometry of Centralized and Distributed Low-rank Matrix Recovery Without Regularization , 2020, IEEE Signal Processing Letters.
[13] Seyedehsara Nayer,et al. Provable Low Rank Phase Retrieval , 2019, IEEE Transactions on Information Theory.
[14] Alexander Gasnikov,et al. Projected Gradient Method for Decentralized Optimization over Time-Varying Networks , 2019, 1911.08527.
[15] Aryan Mokhtari,et al. An Exact Quantized Decentralized Gradient Descent Algorithm , 2018, IEEE Transactions on Signal Processing.
[16] Justin Romberg,et al. Decentralized sketching of low rank matrices , 2019, NeurIPS.
[17] Wotao Yin,et al. On Nonconvex Decentralized Gradient Descent , 2016, IEEE Transactions on Signal Processing.
[18] Prateek Jain,et al. Nearly Optimal Robust Matrix Completion , 2016, ICML.
[19] Behrouz Touri,et al. Non-Convex Distributed Optimization , 2015, IEEE Transactions on Automatic Control.
[20] Constantine Caramanis,et al. Fast Algorithms for Robust PCA via Gradient Descent , 2016, NIPS.
[21] Eric Moulines,et al. D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[22] Gesualdo Scutari,et al. NEXT: In-Network Nonconvex Optimization , 2016, IEEE Transactions on Signal and Information Processing over Networks.
[23] Qing Ling,et al. On the Convergence of Decentralized Gradient Descent , 2013, SIAM J. Optim..
[24] Angelia Nedic,et al. Convergence Rate of Distributed Averaging Dynamics and Optimization in Networks , 2015, Found. Trends Syst. Control..
[25] Yuxin Chen,et al. Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems , 2015, NIPS.
[26] Qing Ling,et al. Decentralized and Privacy-Preserving Low-Rank Matrix Completion , 2015 .
[27] Prateek Jain,et al. Fast Exact Matrix Completion with Finite Samples , 2014, COLT.
[28] Wei Shi,et al. EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization , 2014, SIAM J. Optim..
[29] Alex Olshevsky,et al. Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control , 2014, 1411.4186.
[30] Shannon M. Hughes,et al. Memory and Computation Efficient PCA via Very Sparse Random Projections , 2014, ICML.
[31] Moritz Hardt,et al. The Noisy Power Method: A Meta Algorithm with Applications , 2013, NIPS.
[32] Prateek Jain,et al. Low-rank matrix completion using alternating minimization , 2012, STOC '13.
[33] Angelia Nedic,et al. Distributed Random Projection Algorithm for Convex Optimization , 2012, IEEE Journal of Selected Topics in Signal Processing.
[34] Pascal Bianchi,et al. Performance of a Distributed Stochastic Approximation Algorithm , 2012, IEEE Transactions on Information Theory.
[35] Pascal Bianchi,et al. Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization , 2011, IEEE Transactions on Automatic Control.
[36] Qing Ling,et al. Decentralized low-rank matrix completion , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[37] Asuman E. Ozdaglar,et al. Distributed Subgradient Methods for Convex Optimization Over Random Networks , 2011, IEEE Transactions on Automatic Control.
[38] Asuman E. Ozdaglar,et al. Constrained Consensus and Optimization in Multi-Agent Networks , 2008, IEEE Transactions on Automatic Control.
[39] Asuman E. Ozdaglar,et al. Distributed Subgradient Methods for Multi-Agent Optimization , 2009, IEEE Transactions on Automatic Control.
[40] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[41] John N. Tsitsiklis,et al. Convergence Speed in Distributed Consensus and Averaging , 2009, SIAM J. Control. Optim..
[42] M. Rudelson,et al. The smallest singular value of a random rectangular matrix , 2008, 0802.3956.
[43] David Kempe,et al. A decentralized algorithm for spectral analysis , 2004, STOC '04.
[44] J. Delvenne,et al. Random walks on graphs , 2004 .
[45] Stephen P. Boyd,et al. Fastest Mixing Markov Chain on a Graph , 2004, SIAM Rev..
[46] Ji-guang Sun. On perturbation bounds for the QR factorization , 1995 .
[47] G. Stewart. Perturbation Bounds for the $QR$ Factorization of a Matrix , 1977 .