no code implementations • 1 Nov 2019 • Fred Lin, Keyur Muzumdar, Nikolay Pavlovich Laptev, Mihai-Valentin Curelea, Seunghak Lee, Sriram Sankar
In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability.
no code implementations • 13 Dec 2017 • George Philipp, Seunghak Lee, Eric P. Xing
Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives.
no code implementations • NeurIPS 2014 • Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing
Distributed machine learning has typically been approached from a data parallel perspective, where big data are partitioned to multiple workers and an algorithm is executed concurrently over different data subsets under various synchronization schemes to ensure speed-up and/or correctness.
no code implementations • 25 Oct 2014 • Seunghak Lee, Eric P. Xing
However, screening for overlapping group lasso remains an open challenge because the overlaps between groups make it infeasible to test each group independently.
no code implementations • 18 Jun 2014 • Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing
When training large machine learning models with many variables or parameters, a single machine is often inadequate since the model may be too large to fit in memory, while training can take a long time even with stochastic updates.
no code implementations • 30 Dec 2013 • Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yao-Liang Yu
What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)?
no code implementations • 19 Dec 2013 • Seunghak Lee, Jin Kyu Kim, Qirong Ho, Garth A. Gibson, Eric P. Xing
Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates.
no code implementations • NeurIPS 2013 • Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing
We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees.
no code implementations • NeurIPS 2010 • Seunghak Lee, Jun Zhu, Eric P. Xing
To understand the relationship between genomic variations among population and complex diseases, it is essential to detect eQTLs which are associated with phenotypic effects.