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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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AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning


Feb 02, 2021
Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman


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Learning Massive Graph Embeddings on a Single Machine


Jan 20, 2021
Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

* Under review 

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Accelerating Deep Learning Inference via Learned Caches


Jan 18, 2021
Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella


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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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Accelerating Deep Learning Inference via Freezing


Feb 07, 2020
Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella

* 11th USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2019 

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Blink: Fast and Generic Collectives for Distributed ML


Oct 11, 2019
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica


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Parity Models: A General Framework for Coding-Based Resilience in ML Inference


May 02, 2019
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman


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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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Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation


Jun 04, 2018
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman


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Hemingway: Modeling Distributed Optimization Algorithms


Feb 20, 2017
Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez

* Presented at ML Systems Workshop at NIPS, Dec 2016 

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KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics


Oct 29, 2016
Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht


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Large Scale Kernel Learning using Block Coordinate Descent


Feb 17, 2016
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht


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MLlib: Machine Learning in Apache Spark


May 26, 2015
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar


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