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Pufferfish: Communication-efficient Models At No Extra Cost


Mar 05, 2021
Hongyi Wang, Saurabh Agarwal, Dimitris Papailiopoulos

* Accepted by MLSys 2021 

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On the Utility of Gradient Compression in Distributed Training Systems


Mar 03, 2021
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris Papailiopoulos


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Permutation-Based SGD: Is Random Optimal?


Feb 19, 2021
Shashank Rajput, Kangwook Lee, Dimitris Papailiopoulos


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Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification


Oct 29, 2020
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris Papailiopoulos


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Attack of the Tails: Yes, You Really Can Backdoor Federated Learning


Jul 09, 2020
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos


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Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient


Jun 14, 2020
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos


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Closing the convergence gap of SGD without replacement


Mar 05, 2020
Shashank Rajput, Anant Gupta, Dimitris Papailiopoulos


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Federated Learning with Matched Averaging


Feb 15, 2020
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, Yasaman Khazaeni

* Accepted by ICLR 2020 

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DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation


Jul 29, 2019
Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos


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Bad Global Minima Exist and SGD Can Reach Them


Jun 06, 2019
Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas


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Convergence and Margin of Adversarial Training on Separable Data


May 22, 2019
Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos


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Does Data Augmentation Lead to Positive Margin?


May 08, 2019
Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris Papailiopoulos

* ICML 2019 

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SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding


Jan 28, 2019
Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos


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A Geometric Perspective on the Transferability of Adversarial Directions


Nov 08, 2018
Zachary Charles, Harrison Rosenberg, Dimitris Papailiopoulos


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ATOMO: Communication-efficient Learning via Atomic Sparsification


Jun 24, 2018
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, Stephen Wright


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DRACO: Byzantine-resilient Distributed Training via Redundant Gradients


Jun 22, 2018
Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos

* Accepted by ICML 2018 

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The Effect of Network Width on the Performance of Large-batch Training


Jun 11, 2018
Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris Papailiopoulos, Paraschos Koutris


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Gradient Coding via the Stochastic Block Model


May 25, 2018
Zachary Charles, Dimitris Papailiopoulos


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Speeding Up Distributed Machine Learning Using Codes


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

* 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

* 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

* 30 pages 

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Bipartite Correlation Clustering -- Maximizing Agreements


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

* 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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