3k citations · 24.2k across the 299 of their papers we have counts for
8 papers · 1 filter
Posteriors, conjugacy, and exponential families for completely random measures
Tamara Broderick, Ashia C. Wilson, Michael I. Jordan
We demonstrate how to calculate posteriors for general CRM-based priors and likelihoods for Bayesian nonparametric models. We further show how to represent Bayesian nonparametric p…
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…
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez +5
To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavi…
Optimality guarantees for distributed statistical estimation
John C. Duchi, Michael I. Jordan, Martin J. Wainwright +1
Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines. To stud…
On the Convergence Rate of Decomposable Submodular Function Minimization
Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
Submodular functions describe a variety of discrete problems in machine learning, signal processing, and computer vision. However, minimizing submodular functions poses a number of…
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou +1
Crowdsourcing is a popular paradigm for effectively collecting labels at low cost. The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy label…