216 citations · 327 across the 10 of their papers we have counts for
6 papers · 1 filter
Learning Sparse Nonparametric DAGs
Xun Zheng, Chen Dan, Bryon Aragam +2
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to…
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep Ravikumar +1
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales supe…
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…
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…
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…