961 citations · 1.1k across the 10 of their papers we have counts for
10 papers
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning
Liam Li, Evan Sparks, Kevin Jamieson +1
Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermedi…
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics
Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan +2
Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requi…
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Francois W. Belletti, Evan R. Sparks, Michael J. Franklin +2
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causa…
Embarrassingly Parallel Time Series Analysis for Large Scale Weak Memory Systems
Francois Belletti, Evan Sparks, Michael Franklin +1
Second order stationary models in time series analysis are based on the analysis of essential statistics whose computations follow a common pattern. In particular, with a map-reduc…
Matrix Computations and Optimization in Apache Spark
Reza Bosagh Zadeh, Xiangrui Meng, Aaron Staple +6
We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark provides abstractions and implementations for distributed matric…
Scientific Computing Meets Big Data Technology: An Astronomy Use Case
Zhao Zhang, Kyle Barbary, Frank Austin Nothaft +5
Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typica…
MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz +13
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's ope…
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries
Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin +2
The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved produc…
MLI: An API for Distributed Machine Learning
Evan R. Sparks, Ameet Talwalkar, Virginia Smith +6
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. I…