Ian E. H. Yen 1 * IANYEN@CS.UTEXAS.EDU Xiangru Huang 1 * XRHUANG@CS.UTEXAS.EDU Kai Zhong 2 ZHONGKAI@ICES.UTEXAS.EDU Pradeep Ravikumar 1,2 PRADEEPR@CS.UTEXAS.EDU Inderjit S. Dhillon 1,2â€¦ (More)

In this paper, we propose a Sparse Random Features algorithm, which learns a sparse non-linear predictor by minimizing an l1-regularized objective function over the Hilbert Space induced from aâ€¦ (More)

State of the art statistical estimators for high-dimensional problems take the form of regularized, and hence non-smooth, convex programs. A key facet of these statistical estimation problems is thatâ€¦ (More)

Extreme Classification comprises multi-class or multi-label prediction where there is a large number of classes, and is increasingly relevant to many real-world applications such as text and imageâ€¦ (More)

Many applications of machine learning involve structured outputs with large domains, where learning of a structured predictor is prohibitive due to repetitive calls to an expensive inference oracle.â€¦ (More)

We consider the class of optimization problems arising from computationally intensive `1-regularized M -estimators, where the function or gradient values are very expensive to compute. A particularâ€¦ (More)

Over the past decades, Linear Programming (LP) has been widely used in different areas and considered as one of the mature technologies in numerical optimization. However, the complexity offered byâ€¦ (More)

Multiple Sequence Alignment and Motif Discovery, known as NP-hard problems, are two fundamental tasks in Bioinformatics. Existing approaches to these two problems are based on either local searchâ€¦ (More)

Exemplar clustering attempts to find a subset of data-points that summarizes the entire data-set in the sense of minimizing the sum of distances from each point to its closest exemplar. It has manyâ€¦ (More)

We consider the class of optimization problems arising from computationally intensive `1-regularized M -estimators, where the function or gradient values are very expensive to compute. A particularâ€¦ (More)