240 citations · 899 across the 24 of their papers we have counts for
7 papers · 1 filter
Gradient Sparsification for Communication-Efficient Distributed Optimization
Jianqiao Wangni, Jialei Wang, Ji Liu +1
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the co…
Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations
Jialei Wang, Tong Zhang
We present novel minibatch stochastic optimization methods for empirical risk minimization problems, the methods efficiently leverage variance reduced first-order and sub-sampled h…
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
Jialei Wang, Lin Xiao
We consider empirical risk minimization of linear predictors with convex loss functions. Such problems can be reformulated as convex-concave saddle point problems, and thus are wel…
Efficient coordinate-wise leading eigenvector computation
Jialei Wang, Weiran Wang, Dan Garber +1
We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product. We establish global convergen…
Stochastic Canonical Correlation Analysis
Chao Gao, Dan Garber, Nathan Srebro +2
We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbi…
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
Jialei Wang, Weiran Wang, Nathan Srebro
We present and analyze an approach for distributed stochastic optimization which is statistically optimal and achieves near-linear speedups (up to logarithmic factors). Our approac…