163 citations · 1.6k across the 92 of their papers we have counts for
9 papers · 1 filter
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval
Qi Qian, Inci M. Baytas, Rong Jin +2
We study the problem of similarity learning and its application to image retrieval with large-scale data. The similarity between pairs of images can be measured by the distances be…
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…