10 citations · 22 across the 4 of their papers we have counts for
Showing 2017 · cs.LGShow all
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cs.LG2017
Gradient Sparsification for Communication-Efficient Distributed Optimization
Jianqiao Wangni, Jialei Wang, Ji Liu +1
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the co…
cs.LG2017
Stochastic Canonical Correlation Analysis
Chao Gao, Dan Garber, Nathan Srebro +2
We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbi…
cs.LG2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
Jialei Wang, Weiran Wang, Nathan Srebro
We present and analyze an approach for distributed stochastic optimization which is statistically optimal and achieves near-linear speedups (up to logarithmic factors). Our approac…