163 citations · 1.2k across the 128 of their papers we have counts for
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Analysis of Distributed Stochastic Dual Coordinate Ascent
Tianbao Yang, Shenghuo Zhu, Rong Jin +1
In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary p…
A New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent
Rong Jin, Tianbao Yang, Shenghuo Zhu
In this manuscript, we analyze the sparse signal recovery (compressive sensing) problem from the perspective of convex optimization by stochastic proximal gradient descent. This vi…
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions
Lijun Zhang, Tianbao Yang, Rong Jin +1
Traditional algorithms for stochastic optimization require projecting the solution at each iteration into a given domain to ensure its feasibility. When facing complex domains, suc…
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
Jianhui Chen, Tianbao Yang, Qihang Lin +2
We consider stochastic strongly convex optimization with a complex inequality constraint. This complex inequality constraint may lead to computationally expensive projections in al…
Sparse Multiple Kernel Learning with Geometric Convergence Rate
Rong Jin, Tianbao Yang, Mehrdad Mahdavi
In this paper, we study the problem of sparse multiple kernel learning (MKL), where the goal is to efficiently learn a combination of a fixed small number of kernels from a large p…