Eric P. Xing

TL;DR: In this article, a general optimization approach, called smoothing proximal gradient method, is proposed to solve the structured sparse regression problems with a smooth convex loss and a wide spectrum of structured-sparsityinducing penalties.

TL;DR: This paper proposes a novel regularized regression approach for detecting eQTLs which takes into account related traits simultaneously while incorporating many regulatory features and results confirm that the model outperforms previous methods for finding eZTLs.

TL;DR: A Bayesian approach to the problem of inferring haplotypes from genotypes of single nucleotide polymorphisms (SNPs) based on a nonparametric prior known as the Dirichlet process is presented, which is reminiscent of parsimony methods in its preference for small haplotype pools.

TL;DR: In this article, the authors present a suite of figures and performance metrics derived from the machine learning community that can be used to judge the quality of such uncertainty estimates, and then show a case study where they judge various methods for predicting density-functional-theory-calculated adsorption energies.

TL;DR: This paper forms the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develops an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization.