71 citations · 282 across the 14 of their papers we have counts for
5 papers · 1 filter
Strategies and Principles of Distributed Machine Learning on Big Data
Eric P. Xing, Qirong Ho, Pengtao Xie +1
The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to dig…
LightLDA: Big Topic Models on Modest Compute Clusters
Jinhui Yuan, Fei Gao, Qirong Ho +6
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized c…
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai +3
We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework. Whenever possible…
Consistent Bounded-Asynchronous Parameter Servers for Distributed ML
Jinliang Wei, Wei Dai, Abhimanu Kumar +3
In distributed ML applications, shared parameters are usually replicated among computing nodes to minimize network overhead. Therefore, proper consistency model must be carefully c…
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…