Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM

By Ching-pei Lee, Dan Roth. 2015

54 citations recorded by the source corpus.

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Citations in the corpus

Federated Learning: Challenges, Methods, and Future Directions (2019, background): “the amount of computation versus communication substantially more ﬂexible. For convex objectives, distributed local-updating primal-dual methods have emerged as a popular way to tackle such a problem [54, 62, 72, 107, 128]. These approaches leverage duality structure to effectively decompose the glo”
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…”
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.”
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features (2016, background): “Actually, almost all existing partitio n- n-feature algorithms satisfy this restriction [14, 11, 9, 12, 7].”
Distributed block-diagonal approximation methods for regularized empirical risk minimization (2017, background): “Special cases of the proposed framework were published earlier as conference and workshop papers (Lee and Roth 2015; Lee et al. 2015).”
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization? (2015, methodology): “ where f′′′ (w)[u]:=limt→0 1 t f ′′(w +tu)−f′′(w)). There has been an enormous interest in large-scale machine learning problems and many parallel [4, 11] or distributed algorithms have been proposed [1, 16, 12, 14, 8]. The main bottleneck in distributed computing –communication– was handled by many”
Group-Based Alternating Direction Method of Multipliers for Distributed Linear Classification (2017, unknown): “Although the previous distributed ADMM algorithms [25], [28] for linear classification have been validated for promising results, they still suffer from several limitations, from either theoretical or practical perspectives: 1) they are normally lacking in theoretical analysis, which is needed to de”
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization (2016, methodology): “(Lee and Roth, 2015) derived an analytical solution of the optimal step size for dual linear support vector machine problems.”