Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers

By Caoxie Zhang, Honglak Lee, Kang G. Shin. 2012

69 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): “For SVM classification, we use stochastic dual coordinate ascent as an internal optimizer, which is shown in Zhang et al. (2012) to have superior performance.”
Distributed optimization with arbitrary local solvers (2015, methodology): “An exception is the approach of Zhang, Lee and Shin [78], which is similar to our approach in spirit, albeit based on ADMM, in that they allow for the subproblems to be solved inexactly.”
Stochastic, Distributed and Federated Optimization for Machine Learning (2017, methodology): “An exception is the approach of Zhang, Lee and Shin [194], which is similar to our approach in spirit, albeit based on ADMM, in that they allow for the subproblems to be solved inexactly.”
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations (2018, methodology): “The datasets for these experiments are generated based on a standard procedure introduced in [22], which is also used in [13]: the xi’s and z are sampled from the uniform distribution in [−1, 1], and yi := sgn(xiz) with probability 0.”
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM (2015, unknown): “Because the main computations in batch solvers for (1) are matrix-vector products that can be naively parallelized, several works have successfully adapted these solvers to distributed environments (Agarwal et al., 2014; Zhang et al., 2012; Zhuang et al., 2015; Lin et al., 2014).”
An efficient distributed learning algorithm based on effective local functional approximations (2013, methodology): “• ADMM: We use the example partitioning formulation of the Alternating Direction Method of Multipliers (ADMM) (Boyd et al., 2011; Zhang et al., 2012).”
Distributed Newton Methods for Regularized Logistic Regression (2015, methodology): “In [Zhang, Lee, and Shin, 2012], they consider a coordinate descent method [Hsieh et al., 2008] and a trust region Newton method [Lin, Weng, and Keerthi, 2008] from the software LIBLINEAR [Fan et al., 2008].”
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent (2013, methodology): “ADMM has been employed for solving machine learning problems in a distributed fashion [3, 23], due to its superior convergences and performances [6, 23].”
A General Analysis of the Convergence of ADMM (2015, unknown): “[32], Meshi and Globerson [24], Wang et al.”
Group-Based Alternating Direction Method of Multipliers for Distributed Linear Classification (2017, methodology): “[25] proposed a novel distributed linear classification method via ADMM (namely disADMM in this paper).”