Papers

My research is supported by a National Science Foundation CAREER award: “Flexible Parsimonious Models for Complex Data” (NSF DMS 1653017). Previously, my research was supported in part by a three-year grant from the National Science Foundation: “High-Dimensional Covariance Estimation via Convex Optimization” (NSF DMS 1405746).

Preprints

  • Jacob Bien (2016) The Simulator: An Engine to Streamline Simulations. In revision. [pdf] [website]

  • Jacob Bien (2016) Graph-Guided Banding of the Covariance Matrix. In revision. [pdf]

  • William Nicholson, Jacob Bien, and David Matteson (2016) Hierarchical Vector Autoregression. In revision. [pdf] [software]

Publications

  • Xiaohan Yan and Jacob Bien (2015) Hierarchical Sparse Modeling: A Choice of Two Regularizers. To appear, Statistical Science. [pdf] [software]

  • Jacob Bien, Irina Gaynanova, Johannes Lederer, and Christian Müller (2016) Non-convex Global Minimization and False Discovery Rate Control for the TREX. To appear, Journal of Computational and Graphical Statistics. [pdf] [software]

  • Guo Yu and Jacob Bien (2017) Learning Local Dependence In Ordered Data. Journal of Machine Learning Research. 18(42), 1-60 [pdf] [software] [vignette]

  • William Nicholson, David Matteson, and Jacob Bien (2017) VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables. International Journal of Forecasting. 33(3), 627-651 [pdf] [software]

  • Yin Lou, Jacob Bien, Rich Caruana, and Johannes Gehrke (2016) Sparse Partially Linear Additive Models. Journal of Computational and Graphical Statistics. 25(4), 1126-1140. [pdf] [software]

  • Jacob Bien, Florentina Bunea, and Luo Xiao (2016) Convex Banding of the Covariance Matrix. Journal of the American Statistics Association. 111(514), 834-845 [pdf] [software] [vignette]

  • Jacob Bien and Daniela Witten (2016) Penalized Estimation in Complex Models. In Bühlmann, Drineas, Kane, van der Laan (Eds.), Handbook of Big Data. Chapman and Hall/CRC Reference. [link]

  • Jacob Bien, Noah Simon, and Robert Tibshirani (2015) Convex Hierarchical Testing of Interactions. Annals of Applied Statistics. 9(1), 27-42. [pdf, supplement] [software]

  • Jacob Bien, Jonathan Taylor, and Robert Tibshirani (2013) A Lasso for Hierarchical Interactions. Annals of Statistics. 41(3), 1111-1141 [pdf] [software]

  • Jacob Bien and Marten Wegkamp (2013) Discussion of “Correlated variables in regression: clustering and sparse estimation” by Bühlmann et al. Journal of Statistical Planning and Inference. 143(11), 1859-1862. [pdf]

  • Jacob Bien and Robert Tibshirani (2011) Hierarchical Clustering with Prototypes via Minimax Linkage. Journal of the American Statistical Association. 106(495), 1075-1084 [pdf] [software]

  • Jacob Bien and Robert Tibshirani (2011) Sparse Estimation of a Covariance Matrix. Biometrika. 98(4), 807-820 [pdf] [software]

  • Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan Tibshirani (2012) Strong Rules for Discarding Predictors in Lasso-type Problems. Journal of the Royal Statistical Society, Series B. 74(2), 245-266 [pdf]

  • Jacob Bien and Robert Tibshirani (2011) Prototype Selection for Interpretable Classification. Annals of Applied Statistics. 5(4), 2403-2424 [pdf] [software]

  • Neema Moraveji, Daniel Russell, Jacob Bien, David Mease (2011) Measuring Improvement in User Search Performance Resulting from Optimal Search Tips. Proceedings of SIGIR 2011. [abstract]

  • Jacob Bien, Ya Xu, and Michael Mahoney (2010) CUR from a Sparse Optimization Viewpoint. Advances in Neural Information Processing Systems 23. [pdf]