Solving Large Scale Linear SVM with Distributed Block Minimization

By Dmitry Pechyony, Libin Shen, Rosie Jones. 2011

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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; Pechyony et al., 2011; Yang, 2013; Lee & Roth, 2015), the COCOA and COCOA+ frameworks also allow more control over the aggregation of updates between ma-”
Distributed optimization with arbitrary local solvers (2015, methodology): “By making use of the primal-dual structure in the line of work of [31, 45, 70–72], the CoCoA and CoCoA + frameworks proposed here are the ﬁrst to allow the use of any local solver — of weak local approximation quality — in each round.”
Stochastic, Distributed and Federated Optimization for Machine Learning (2017, methodology): “By making use of the primal-dual structure in the line of work of [191, 137, 188, 189, 94], the CoCoA and CoCoA+ frameworks proposed here are the first to allow the use of any local solver—of weak local approximation quality—in each round.”
Distributed block-diagonal approximation methods for regularized empirical risk minimization (2017, background): “For a t 1, practical results [35,49] suggest that a1 ∈ [1,K] leads to good empirical performance, while we prefer a1 ≡ 1 as it is a closer approximation to the Hessian.”
An efficient distributed learning algorithm based on effective local functional approximations (2013, methodology): “, 2014) represents the class of distributed dual methods (Pechyony et al., 2011; Yang, 2013; Yang et al., 2013; Jaggi et al., 2014) that, in each outer iteration, solve (in parallel) several local dual optimization problems.”
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent (2015, methodology): “, 2011) and synchronized DCD algorithms (Pechyony et al., 2011; Yang, 2013; Jaggi et al., 2014).”
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM (2015, unknown): “Our algorithm is closely related to the methods proposed by Pechyony et al. (2011) approaches were originally discussed in different ways, they can be described using our framework.”