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Tilted Empirical Risk Minimization

Jul 02, 2020
Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith


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FedDANE: A Federated Newton-Type Method

Jan 07, 2020
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

* Asilomar Conference on Signals, Systems, and Computers 2019 

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Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches

Dec 06, 2019
Tian Li, Zaoxing Liu, Vyas Sekar, Virginia Smith


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Enhancing the Privacy of Federated Learning with Sketching

Nov 05, 2019
Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar


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Progressive Compressed Records: Taking a Byte out of Deep Learning Data

Nov 01, 2019
Michael Kuchnik, George Amvrosiadis, Virginia Smith


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Federated Learning: Challenges, Methods, and Future Directions

Aug 21, 2019
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith


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Fair Resource Allocation in Federated Learning

May 25, 2019
Tian Li, Maziar Sanjabi, Virginia Smith


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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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One-Shot Federated Learning

Mar 05, 2019
Neel Guha, Ameet Talwalkar, Virginia Smith

* 5 pages, 3 figures, 1 table. 2nd Workshop on Machine Learning on the Phone and other Consumer Devices, NeurIPs 2018 

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LEAF: A Benchmark for Federated Settings

Jan 09, 2019
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar


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On the Convergence of Federated Optimization in Heterogeneous Networks

Dec 14, 2018
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith

* Preprint. Work in Progress 

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Efficient Augmentation via Data Subsampling

Oct 11, 2018
Michael Kuchnik, Virginia Smith


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CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Oct 10, 2018
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I. Jordan, Martin Jaggi


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A Kernel Theory of Modern Data Augmentation

Mar 16, 2018
Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré


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Federated Multi-Task Learning

Feb 27, 2018
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar


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Distributed Optimization with Arbitrary Local Solvers

Aug 03, 2016
Chenxin Ma, Jakub Konečný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takáč


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L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework

Jun 02, 2016
Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi


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Adding vs. Averaging in Distributed Primal-Dual Optimization

Jul 03, 2015
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takáč

* ICML 2015: JMLR W&CP volume37, Proceedings of The 32nd International Conference on Machine Learning, pp. 1973-1982 

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Communication-Efficient Distributed Dual Coordinate Ascent

Sep 29, 2014
Martin Jaggi, Virginia Smith, Martin Takáč, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan

* NIPS 2014 version, including proofs. Published in Advances in Neural Information Processing Systems 27 (NIPS 2014) 

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MLI: An API for Distributed Machine Learning

Oct 25, 2013
Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska


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