216 citations · 327 across the 10 of their papers we have counts for
10 papers
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…
Efficient Tensor Decomposition with Boolean Factors
Sung-En Chang, Xun Zheng, Ian E. H. Yen +2
Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors. The r…
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…
State Space LSTM Models with Particle MCMC Inference
Xun Zheng, Manzil Zaheer, Amr Ahmed +3
Long Short-Term Memory (LSTM) is one of the most powerful sequence models. Despite the strong performance, however, it lacks the nice interpretability as in state space models. In…
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…
Model-Parallel Inference for Big Topic Models
Xun Zheng, Jin Kyu Kim, Qirong Ho +1
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so…
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…
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Jun Zhu, Xun Zheng, Bo Zhang
Supervised topic models with a logistic likelihood have two issues that potentially limit their practical use: 1) response variables are usually over-weighted by document word coun…