163 citations · 1.2k across the 128 of their papers we have counts for
9 papers · 1 filter
Stochastic Proximal Gradient Descent for Nuclear Norm Regularization
Lijun Zhang, Tianbao Yang, Rong Jin +1
In this paper, we utilize stochastic optimization to reduce the space complexity of convex composite optimization with a nuclear norm regularizer, where the variable is a matrix of…
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
Tianbao Yang, Qihang Lin
In this paper, we study the efficiency of a {\bf R}estarted {\bf S}ub{\bf G}radient (RSG) method that periodically restarts the standard subgradient method (SG). We show that, when…
Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach
Lijun Zhang, Tianbao Yang, Rong Jin +1
In this paper, we develop a randomized algorithm and theory for learning a sparse model from large-scale and high-dimensional data, which is usually formulated as an empirical risk…
Online Stochastic Linear Optimization under One-bit Feedback
Lijun Zhang, Tianbao Yang, Rong Jin +1
In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is revealed to the learner at each round. This problem…
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem
Adams Wei Yu, Qihang Lin, Tianbao Yang
We propose a doubly stochastic primal-dual coordinate optimization algorithm for empirical risk minimization, which can be formulated as a bilinear saddle-point problem. In each it…
Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees
Tianbao Yang, Lijun Zhang, Qihang Lin +1
In this paper, we study a fast approximation method for {\it large-scale high-dimensional} sparse least-squares regression problem by exploiting the Johnson-Lindenstrauss (JL) tran…