high-dimensional regression

Sparse Single-Index Model JMLR 2013 A Comparison of the Lasso and Marginal Regression JMLR 2012 Consistent Model Selection Criteria on High Dimensions JMLR 2012 Nonparametric Reduced Rank Regression NeurIPS 2012 Private Convex Empirical Risk Minimization and High-dimensional Regression COLT 2012 Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions NeurIPS 2012 Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality JMLR 2012 High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity NeurIPS 2011 Sparse recovery by thresholded non-negative least squares NeurIPS 2011 Tight conditions for consistent variable selection in high dimensional nonparametric regression COLT 2011 A Dirty Model for Multi-task Learning NeurIPS 2010 Multivariate Dyadic Regression Trees for Sparse Learning Problems NeurIPS 2010 Rate Minimaxity of the Lasso and Dantzig Selector for the Loss in Balls JMLR 2010 Compressed Least-Squares Regression NeurIPS 2009 High-dimensional support union recovery in multivariate regression NeurIPS 2008 SpAM: Sparse Additive Models NeurIPS 2007 Quantile Regression Forests JMLR 2006