440 citations · 1.4k across the 24 of their papers we have counts for
8 papers · 1 filter
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…
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…
Parallel Coordinate Descent for L1-Regularized Loss Minimization
Joseph K. Bradley, Aapo Kyrola, Danny Bickson +1
We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1-regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds…
Inference with Multivariate Heavy-Tails in Linear Models
Danny Bickson, Carlos Guestrin
Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which inv…