On Theoretical Analysis of Distributed Stochastic Dual Coordinate Ascent

By Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin. 2013

8 citations recorded by the source corpus.

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Citations in the corpus

CoCoA: A General Framework for Communication-Efficient Distributed Optimization (2016, methodology): “By making use of the primal-dual structure in the line of work of Yu et al. (2012); Pechyony et al. (2011); Yang (2013); Yang et al. (2013) and Lee and Roth (2015), the CoCoA-v1 and CoCoA+ frameworks (which are special cases of the presented framework, CoCoA) are the first to allow the use of any…”
Adding vs. Averaging in Distributed Primal-Dual Optimization (2015, methodology): “By making use of the primal-dual structure in the line of work of (Yu et al., 2012; Shalev-Shwartz & Zhang, 2013c; Yang, 2013; Yang et al., 2013; Jaggi et al., 2014), the C O C O A framework as well as the extension here allow to have more control over the meaningful aggregation of updates between…”
Communication-Efficient Distributed Dual Coordinate Ascent (2014, methodology): “We generalize the algorithm of [2, 3] by allowing the use of arbitrary (dual) optimization methods as the local subroutine within our framework.”
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM (2015, unknown): “Yang et al. (2014) provides convergence for this variant only under the unrealistic assumption ¯ Q = ˜ Q. Recently, Ma et al. (2015) show that when L2 regularization is used, for both DSVM-AVE and the practical variant of DisDCA, the number of *: Among the feature dimensions, only 680, 715…”
Distributed Mini-Batch SDCA (2015, methodology): “In this Section we will make use of the Expected Separable Overapproximation (ESO) theory introduced in [20] and further extended e.g. in [16, 19, 18].”