70 citations · 560 across the 53 of their papers we have counts for
8 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…
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…
Towards Making High Dimensional Distance Metric Learning Practical
Qi Qian, Rong Jin, Lijun Zhang +1
In this work, we study distance metric learning (DML) for high dimensional data. A typical approach for DML with high dimensional data is to perform the dimensionality reduction fi…
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…
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
Tianbao Yang, Lijun Zhang, Rong Jin +1
In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a…
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion
Lijun Zhang, Tianbao Yang, Rong Jin +1
In this paper, we develop a relative error bound for nuclear norm regularized matrix completion, with the focus on the completion of full-rank matrices. Under the assumption that t…
Theory of Dual-sparse Regularized Randomized Reduction
Tianbao Yang, Lijun Zhang, Rong Jin +1
In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e.g., random projection, random hashi…