31 citations · 66 across the 8 of their papers we have counts for
8 papers
Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment
Fred Lin, Keyur Muzumdar, Nikolay Pavlovich Laptev +3
Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a…
Stability Selection for Structured Variable Selection
George Philipp, Seunghak Lee, Eric P. Xing
In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stabil…
Screening Rules for Overlapping Group Lasso
Seunghak Lee, Eric P. Xing
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero c…
Primitives for Dynamic Big Model Parallelism
Seunghak Lee, Jin Kyu Kim, Xun Zheng +3
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 trainin…
Structure-Aware Dynamic Scheduler for Parallel Machine Learning
Seunghak Lee, Jin Kyu Kim, Qirong Ho +2
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. A natural solu…
Petuum: A New Platform for Distributed Machine Learning on Big Data
Eric P. Xing, Qirong Ho, Wei Dai +7
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 D…
Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity
Seunghak Lee, Eric P. Xing
We develop a highly scalable optimization method called "hierarchical group-thresholding" for solving a multi-task regression model with complex structured sparsity constraints on…
Structured Input-Output Lasso, with Application to eQTL Mapping, and a Thresholding Algorithm for Fast Estimation
Seunghak Lee, Eric P. Xing
We consider the problem of learning a high-dimensional multi-task regression model, under sparsity constraints induced by presence of grouping structures on the input covariates an…