Distributed Newton Methods for Regularized Logistic Regression

By Yong Zhuang, Wei-Sheng Chin, Yu-Chin Juan, Chih-Jen Lin. 2015

46 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Federated Optimization: Distributed Machine Learning for On-Device Intelligence (2016, methodology): “The DiSCO algorithm is related to [52, 106], a distributed truncated Newton method.”
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss (2015, background): “Under the assumption that each local function fi is L-smooth and λ-strongly convex, the ADMM approach can achieve linear convergence, and the best known complexity is O(√κ log(1/ǫ)) [17].”
Stochastic, Distributed and Federated Optimization for Machine Learning (2017, methodology): “The DiSCO algorithm is related to [98, 202], a distributed truncated Newton method.”
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss (2015, methodology): “A similar approach was proposed by Zhuang et al. (2014) and Lin et al. (2014) for ERM of linear predictors, where they use a distributed truncated Newton method and conjugate gradient (CG) method for solving the Newton system.”
Distributed block-diagonal approximation methods for regularized empirical risk minimization (2017, methodology): “Among them, it has been shown that distributed truncated-Newton [54,26], distributed limited-memory BFGS [6], and the limited-memory common-directions method [21] are the most efficient ones in practice.”
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).”
Large-scale logistic regression and linear support vector machines using spark (2014, unknown): “[7] Kuan-Wei Wu, Chun-Sung Ferng, Chia-Hua Ho, An-Chun Liang, Chun-Heng Huang, Wei-Yuan Shen, Jyun-Yu Jiang, Ming-Hao Yang, Ting-Wei Lin, Ching-pei Lee, Perng-Hwa Kung, Chin-En Wang, TingWei Ku, Chun-Yen Ho, Yi-Shu Tai, I-Kuei Chen, Wei-Lun Huang, Che-Ping Chou, Tse-Ju Lin, Han-Jay Yang, Yen-Kai Wan”
Distributed coordinate descent for generalized linear models with regularization (2016, methodology): “On the large scale this problem is typically solved by the conjugate gradient method, “Limited memory BFGS” (L-BFGS) [10, 31], TRON [36].”