More effective distributed ml via a stale synchronous parallel parameter server Q Ho, J Cipar, H Cui, S Lee, JK Kim, PB Gibbons, GA Gibson, G Ganger, ... Advances in neural information processing systems 26, 2013 | 926 | 2013 |
Petuum: A new platform for distributed machine learning on big data EP Xing, Q Ho, W Dai, JK Kim, J Wei, S Lee, X Zheng, P Xie, A Kumar, ... Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 632 | 2015 |
Exploiting bounded staleness to speed up big data analytics H Cui, J Cipar, Q Ho, JK Kim, S Lee, A Kumar, J Wei, W Dai, GR Ganger, ... 2014 USENIX Annual Technical Conference (USENIX ATC 14), 37-48, 2014 | 210 | 2014 |
Identification of individuals by trait prediction using whole-genome sequencing data C Lippert, R Sabatini, MC Maher, EY Kang, S Lee, O Arikan, A Harley, ... Proceedings of the National Academy of Sciences 114 (38), 10166-10171, 2017 | 197 | 2017 |
Solving the straggler problem with bounded staleness J Cipar, Q Ho, JK Kim, S Lee, GR Ganger, G Gibson, K Keeton, E Xing 14th Workshop on Hot Topics in Operating Systems (HotOS XIV), 2013 | 170 | 2013 |
MoDIL: detecting small indels from clone-end sequencing with mixtures of distributions S Lee, F Hormozdiari, C Alkan, M Brudno Nature methods 6 (7), 473-474, 2009 | 162 | 2009 |
On model parallelization and scheduling strategies for distributed machine learning S Lee, JK Kim, X Zheng, Q Ho, GA Gibson, EP Xing Advances in neural information processing systems 27, 2014 | 161 | 2014 |
Adaptive multi-task lasso: with application to eQTL detection S Lee, J Zhu, E Xing Advances in neural information processing systems 23, 2010 | 116 | 2010 |
Strads: A distributed framework for scheduled model parallel machine learning JK Kim, Q Ho, S Lee, X Zheng, W Dai, GA Gibson, EP Xing Proceedings of the Eleventh European Conference on Computer Systems, 1-16, 2016 | 109 | 2016 |
SDD: high performance code clone detection system for large scale source code S Lee, I Jeong Companion to the 20th annual ACM SIGPLAN conference on Object-oriented …, 2005 | 95 | 2005 |
A robust framework for detecting structural variations in a genome S Lee, E Cheran, M Brudno Bioinformatics 24 (13), i59-i67, 2008 | 78 | 2008 |
Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs S Lee, EP Xing Bioinformatics 28 (12), i137-i146, 2012 | 60 | 2012 |
Petuum: A framework for iterative-convergent distributed ml W Dai, J Wei, X Zheng, JK Kim, S Lee, J Yin, Q Ho, EP Xing arXiv preprint arXiv:1312.7651 1 (2.1), 2013 | 43 | 2013 |
Dynamically Weighted Hidden Markov Model for Spam Deobfuscation. S Lee, I Jeong, S Choi IJCAI 7, 2523-2529, 2007 | 23 | 2007 |
MoGUL: detecting common insertions and deletions in a population S Lee, E Xing, M Brudno Annual International Conference on Research in Computational Molecular …, 2010 | 21 | 2010 |
Ensembles of Lasso screening rules S Lee, N Görnitz, EP Xing, D Heckerman, C Lippert IEEE transactions on pattern analysis and machine intelligence 40 (12), 2841 …, 2017 | 18 | 2017 |
Screening rules for overlapping group lasso S Lee, EP Xing arXiv preprint arXiv:1410.6880, 2014 | 18 | 2014 |
A network-driven approach for genome-wide association mapping S Lee, S Kong, EP Xing Bioinformatics 32 (12), i164-i173, 2016 | 11 | 2016 |
Primitives for dynamic big model parallelism S Lee, JK Kim, X Zheng, Q Ho, GA Gibson, EP Xing arXiv preprint arXiv:1406.4580, 2014 | 10 | 2014 |
GWAS in a box: statistical and visual analytics of structured associations via GenAMap EP Xing, RE Curtis, G Schoenherr, S Lee, J Yin, K Puniyani, W Wu, ... PLoS One 9 (6), e97524, 2014 | 9 | 2014 |