Jacob Bien
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Strong rules for discarding predictors in lasso-type problems
R Tibshirani, J Bien, J Friedman, T Hastie, N Simon, J Taylor, ...
Journal of the Royal Statistical Society: Series B 74 (2), 245-266, 2010
A lasso for hierarchical interactions
J Bien, J Taylor, R Tibshirani
Annals of statistics 41 (3), 1111, 2013
Sparse Estimation of a Covariance Matrix
J Bien, R Tibshirani
Biometrika 98 (4), 807-820, 2011
Prototype Selection for Interpretable Classification
J Bien, R Tibshirani
Annals of Applied Statistics 5 (4), 2403-2424, 2011
Hierarchical clustering with prototypes via minimax linkage
J Bien, R Tibshirani
Journal of the American Statistical Association 106 (495), 1075-1084, 2011
VARX-L: Structured regularization for large vector autoregressions with exogenous variables
WB Nicholson, DS Matteson, J Bien
International Journal of Forecasting 33 (3), 627-651, 2017
High dimensional forecasting via interpretable vector autoregression
WB Nicholson, I Wilms, J Bien, DS Matteson
arXiv preprint arXiv:1412.5250, 2014
CUR from a sparse optimization viewpoint
J Bien, Y Xu, MW Mahoney
Advances in Neural Information Processing Systems 23, 2010
Sparse partially linear additive models
Y Lou, J Bien, R Caruana, J Gehrke
Journal of Computational and Graphical Statistics 25 (4), 1126-1140, 2016
Measuring improvement in user search performance resulting from optimal search tips
N Moraveji, D Russell, J Bien, D Mease
Proceedings of the 34th international ACM SIGIR conference on Research and …, 2011
Hierarchical sparse modeling: A choice of two group lasso formulations
X Yan, J Bien
Statistical Science 32 (4), 531-560, 2017
Convex banding of the covariance matrix
J Bien, F Bunea, L Xiao
Journal of the American Statistical Association 111 (514), 834-845, 2016
Structured regularization for large vector autoregressions
WB Nicholson, DS Matteson, J Bien
Cornell University, 2014
Covariate-assisted ranking and screening for large-scale two-sample inference
TT Cai, W Sun, W Wang
Royal Statistical Society 81 (2), 2019
Convex hierarchical testing of interactions
J Bien, N Simon, R Tibshirani
The Annals of Applied Statistics, 27-42, 2015
Learning local dependence in ordered data
G Yu, J Bien
The Journal of Machine Learning Research 18 (1), 1354-1413, 2017
Rare feature selection in high dimensions
X Yan, J Bien
Journal of the American Statistical Association, 1-30, 2020
Non-convex global minimization and false discovery rate control for the TREX
J Bien, I Gaynanova, J Lederer, CL Müller
Journal of Computational and Graphical Statistics 27 (1), 23-33, 2018
The simulator: an engine to streamline simulations
J Bien
arXiv preprint arXiv:1607.00021, 2016
Estimating the error variance in a high-dimensional linear model
G Yu, J Bien
Biometrika 106 (3), 533-546, 2019
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