Evan Randall Sparks

According to our database1, Evan Randall Sparks authored at least 19 papers between 2008 and 2021.

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Bibliography

2021

2020
Kira: Processing Astronomy Imagery Using Big Data Technology.
IEEE Trans. Big Data, 2020

2019
SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning.
CoRR, 2019

2017
Paleo: A Performance Model for Deep Neural Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics.
Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Diagnosing Machine Learning Pipelines with Fine-grained Lineage.
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.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
End-to-End Large Scale Machine Learning with KeystoneML.
PhD thesis, 2016

MLlib: Machine Learning in Apache Spark.
J. Mach. Learn. Res., 2016

Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies.
CoRR, 2016

Matrix Computations and Optimization in Apache Spark.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015
linalg: Matrix Computations in Apache Spark.
CoRR, 2015

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries.
CoRR, 2015

Embarrassingly Parallel Time Series Analysis for Large Scale Weak Memory Systems.
CoRR, 2015

Automating model search for large scale machine learning.
Proceedings of the Sixth ACM Symposium on Cloud Computing, 2015

Scientific computing meets big data technology: An astronomy use case.
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.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

2008
TOCTOU, Traps, and Trusted Computing.
Proceedings of the Trusted Computing, 2008


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