KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

no code implementations • 29 Oct 2016 • Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht

Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements.

BIG-bench Machine Learning General Classification +1

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

no code implementations • 10 Mar 2016 • Francois W. Belletti, Evan R. Sparks, Michael J. Franklin, Alexandre M. Bayen, Joseph E. Gonzalez

Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering.

Causal Inference Time Series +1

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

no code implementations • 31 Jan 2015 • Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin, Michael. I. Jordan, Tim Kraska

The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces.

MLI: An API for Distributed Machine Learning

no code implementations • 21 Oct 2013 • Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph Gonzalez, Michael J. Franklin, Michael. I. Jordan, Tim Kraska

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing.

BIG-bench Machine Learning