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- University of California, BerkeleyUS131 papers
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9 papers · 1 filter
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-q…
Communication-Efficient Distributed Dual Coordinate Ascent
Martin Jaggi, Virginia Smith, Martin Takáč +4
Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper, we propose a commun…
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…
Online Local Learning via Semidefinite Programming
Paul Christiano
In many online learning problems we are interested in predicting local information about some universe of items. For example, we may want to know whether two items are in the same…
Distributed Non-Stochastic Experts
Varun Kanade, Zhenming Liu, Bozidar Radunovic
We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to sites, and the sites are r…
Variational Bayesian Inference with Stochastic Search
John Paisley, David Blei, Michael Jordan
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by m…