440 citations · 884 across the 6 of their papers we have counts for
7 papers
GraphLab: A New Framework For Parallel Machine Learning
Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola +3
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuff…
Distributed Parallel Inference on Large Factor Graphs
Joseph E. Gonzalez, Yucheng Low, Carlos E. Guestrin +1
As computer clusters become more common and the size of the problems encountered in the field of AI grows, there is an increasing demand for efficient parallel inference algorithms…
Distributed GraphLab: A Framework for Machine Learning in the Cloud
Yucheng Low, Joseph Gonzalez, Aapo Kyrola +3
While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently suppo…
Efficient Multicore Collaborative Filtering
Yao Wu, Qiang Yan, Danny Bickson +2
This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique…
GraphLab: A Distributed Framework for Machine Learning in the Cloud
Yucheng Low, Joseph Gonzalez, Aapo Kyrola +2
Machine Learning (ML) techniques are indispensable in a wide range of fields. Unfortunately, the exponential increase of dataset sizes are rapidly extending the runtime of sequenti…
Kernel Belief Propagation
Le Song, Arthur Gretton, Danny Bickson +2
We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a repr…