3k citations · 24.2k across the 299 of their papers we have counts for
22 papers · 1 filter
Distributed Optimization with Arbitrary Local Solvers
Chenxin Ma, Jakub Konečný, Martin Jaggi +4
With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Rece…
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Virginia Smith, Simone Forte, Michael I. Jordan +1
Despite the importance of sparsity in many large-scale applications, there are few methods for distributed optimization of sparsity-inducing objectives. In this paper, we present a…
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang, Jason D. Lee, Martin J. Wainwright +1
We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are -Lipschitz continuous, we present algorithms to learn ha…
SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica +1
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a clus…
Asynchronous Complex Analytics in a Distributed Dataflow Architecture
Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan +4
Scalable distributed dataflow systems have recently experienced widespread adoption, with commodity dataflow engines such as Hadoop and Spark, and even commodity SQL engines routin…
-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan
We study the improper learning of multi-layer neural networks. Suppose that the neural network to be learned has hidden layers and that the -norm of the incoming weight…