961 citations · 983 across the 3 of their papers we have counts for
5 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…
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…
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…