Seunghak Lee
Carnegie Mellon University
36 Papers
262 Citations
Seunghak Lee is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Lasso (statistics). The author has an hindex of 16, co-authored 35 publications. Previous affiliations of Seunghak Lee include Pohang University of Science and Technology & University of Toronto.
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Papers
•Proceedings Article
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Qirong Ho,James Cipar,Henggang Cui,Seunghak Lee,Jin-Kyu Kim,Phillip B. Gibbons,Garth A. Gibson,Greg Ganger,Eric P. Xing +8 more
- 05 Dec 2013
TL;DR: 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.
Petuum: A New Platform for Distributed Machine Learning on Big Data
Eric P. Xing,Qirong Ho,Wei Dai,Jin-Kyu Kim,Jinliang Wei,Seunghak Lee,Xun Zheng,Pengtao Xie,Abhimanu Kumar,Yaoliang Yu +9 more
TL;DR: This work proposes a general-purpose framework, Petuum, that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions.
534
•Posted Content
Petuum: A New Platform for Distributed Machine Learning on Big Data
Eric P. Xing,Qirong Ho,Wei Dai,Jin-Kyu Kim,Jinliang Wei,Seunghak Lee,Xun Zheng,Pengtao Xie,Abhimanu Kumar,Yaoliang Yu +9 more
TL;DR: In this article, the authors propose a general-purpose framework that systematically addresses data and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions.
186
Identification of individuals by trait prediction using whole-genome sequencing data
Christoph Lippert,Riccardo Sabatini,M. Cyrus Maher,Eun Yong Kang,Seunghak Lee,Okan Arikan,Alena Harley,Axel Bernal,Peter Garst,Victor Lavrenko,Kenneth Yocum,Theodore M. Wong,Mingfu Zhu,Wen-Yun Yang,Christopher J. Chang,Timothy T. Lu,Charlie W. H. Lee,Barry W. Hicks,Smriti Ramakrishnan,Haibao Tang,Chao Xie,Jason Piper,Suzanne Brewerton,Yaron Turpaz,Amalio Telenti,Rhonda K. Roby,Franz Josef Och,J. Craig Venter +27 more
TL;DR: A maximum entropy algorithm is developed that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person and may have far-reaching ethical and legal implications.
172
Petuum: A New Platform for Distributed Machine Learning on Big Data
Eric P. Xing,Qirong Ho,Wei Dai,Jin-Kyu Kim,Jinliang Wei,Seunghak Lee,Xun Zheng,Pengtao Xie,Abhimanu Kumar,Yaoliang Yu +9 more
- 10 Aug 2015
TL;DR: This work proposes a general-purpose framework, Petuum, that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions.