Model Assertions for Monitoring and Improving ML Models

Mar 11, 2020
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia

* MLSys 2020 

  Access Model/Code and Paper
MLPerf Training Benchmark

Oct 30, 2019
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia


  Access Model/Code and Paper
Selection Via Proxy: Efficient Data Selection For Deep Learning

Jun 26, 2019
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia


  Access Model/Code and Paper
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference

Jun 03, 2019
Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia


  Access Model/Code and Paper
CrossTrainer: Practical Domain Adaptation with Loss Reweighting

May 07, 2019
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis


  Access Model/Code and Paper
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


  Access Model/Code and Paper
Equivariant Transformer Networks

Jan 25, 2019
Kai Sheng Tai, Peter Bailis, Gregory Valiant


  Access Model/Code and Paper
LIT: Block-wise Intermediate Representation Training for Model Compression

Oct 02, 2018
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia


  Access Model/Code and Paper
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark

Jun 04, 2018
Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Re, Matei Zaharia


  Access Model/Code and Paper
Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data

May 30, 2018
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

* 17 pages 

  Access Model/Code and Paper
Sketching Linear Classifiers over Data Streams

Apr 06, 2018
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant

* Full version of paper appearing at SIGMOD 2018 with more detailed proofs of theoretical results. Code available at https://github.com/stanford-futuredata/wmsketch 

  Access Model/Code and Paper
NoScope: Optimizing Neural Network Queries over Video at Scale

Aug 08, 2017
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

* PVLDB 2017 

  Access Model/Code and Paper
Infrastructure for Usable Machine Learning: The Stanford DAWN Project

Jun 09, 2017
Peter Bailis, Kunle Olukotun, Christopher Re, Matei Zaharia


  Access Model/Code and Paper