961 citations · 1.1k across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…