Evan Randall Sparks

According to our database, Evan Randall Sparks authored at least 19 papers between 2008 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2021

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale.

Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks

Proceedings of the NeurIPS 2022 Competition Track, 2021

2020

Kira: Processing Astronomy Imagery Using Big Data Technology.

Zhao Zhang, Kyle Barbary, Frank Austin Nothaft, Evan Randall Sparks, Oliver Zahn, Michael J. Franklin, David A. Patterson, Saul Perlmutter

IEEE Trans. Big Data, 2020

2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. 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 M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

CoRR, 2019

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning.

Liam Li, Evan Randall Sparks, Kevin G. Jamieson, Ameet Talwalkar

CoRR, 2019

2017

Paleo: A Performance Model for Deep Neural Networks.

Hang Qi, Evan Randall Sparks, Ameet Talwalkar

Proceedings of the 5th International Conference on Learning Representations, 2017

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics.

Evan Randall Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Diagnosing Machine Learning Pipelines with Fine-grained Lineage.

Zhao Zhang, Evan Randall Sparks, Michael J. Franklin

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, 2017

Random projection design for scalable implicit smoothing of randomly observed stochastic processes.

Francois Belletti, Evan Randall Sparks, Alexandre M. Bayen, Joseph Gonzalez

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

End-to-End Large Scale Machine Learning with KeystoneML.

Evan Randall Sparks

PhD thesis, 2016

MLlib: Machine Learning in Apache Spark.

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

J. Mach. Learn. Res., 2016

Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies.

Francois W. Belletti, Evan Randall Sparks, Michael J. Franklin, Alexandre M. Bayen, Joseph E. Gonzalez

CoRR, 2016

Matrix Computations and Optimization in Apache Spark.

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Aaron Staple, Matei Zaharia

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

linalg: Matrix Computations in Apache Spark.

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Alexander Ulanov, Matei Zaharia

CoRR, 2015

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries.

Evan Randall Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska

CoRR, 2015

Embarrassingly Parallel Time Series Analysis for Large Scale Weak Memory Systems.

Francois Belletti, Evan Randall Sparks, Michael J. Franklin, Alexandre M. Bayen

CoRR, 2015

Automating model search for large scale machine learning.

Evan Randall Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska

Proceedings of the Sixth ACM Symposium on Cloud Computing, 2015

Scientific computing meets big data technology: An astronomy use case.

Zhao Zhang, Kyle Barbary, Frank Austin Nothaft, Evan Randall Sparks, Oliver Zahn, Michael J. Franklin, David A. Patterson, Saul Perlmutter

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

2013

MLI: An API for Distributed Machine Learning.

Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

2008

TOCTOU, Traps, and Trusted Computing.

Sergey Bratus, Nihal D'Cunha, Evan Randall Sparks, Sean W. Smith

Proceedings of the Trusted Computing, 2008