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Speeding Up Distributed Machine Learning Using Codes


Jan 29, 2018
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran

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* This work is published in IEEE Transactions on Information Theory and presented in part at the NIPS 2015 Workshop on Machine Learning Systems and the IEEE ISIT 2016 

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Gradient Diversity: a Key Ingredient for Scalable Distributed Learning


Jan 07, 2018
Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, Peter Bartlett

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Approximate Gradient Coding via Sparse Random Graphs


Nov 17, 2017
Zachary Charles, Dimitris Papailiopoulos, Jordan Ellenberg

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Stability and Generalization of Learning Algorithms that Converge to Global Optima


Oct 23, 2017
Zachary Charles, Dimitris Papailiopoulos

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* 27 pages, 5 figures 

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CYCLADES: Conflict-free Asynchronous Machine Learning


May 31, 2016
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht

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Perturbed Iterate Analysis for Asynchronous Stochastic Optimization


Mar 25, 2016
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan

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* 30 pages 

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Bipartite Correlation Clustering -- Maximizing Agreements


Mar 09, 2016
Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros G. Dimakis

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* To appear in AISTATS 2016 

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Sparse PCA via Bipartite Matchings


Aug 04, 2015
Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis

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On the Worst-Case Approximability of Sparse PCA


Jul 21, 2015
Siu On Chan, Dimitris Papailiopoulos, Aviad Rubinstein

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* 20 pages 

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Parallel Correlation Clustering on Big Graphs


Jul 20, 2015
Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan

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