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Papers

My research is supported in part by the National Science Foundation (CAREER award DMS-1653017), the National Institutes of Health (R01 GM123993), and the Simons Foundation. Previously, my research was supported in part by a three-year grant from the National Science Foundation (NSF DMS-1405746).

Preprints

  • Gregory Faletto and Jacob Bien (2022) Cluster Stability Selection. [pdf]

  • Simeng Shao, Jacob Bien, and Adel Javanmard (2021) Controlling the False Split Rate in Tree-Based Aggregation. [pdf] [software]

  • Guo Yu, Jacob Bien, and Ryan Tibshirani (2019) Reluctant Interaction Modeling. [pdf] [software]

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

Publications

  • Ines Wilms and Jacob Bien (2022) Tree-based Node Aggregation in Sparse Graphical Models. Accepted, Journal of Machine Learning Research [pdf] [software]

  • Lucy Gao, Jacob Bien, and Daniela Witten (2022) Selective Inference for Hierarchical Clustering. Accepted, Journal of the American Statistical Association [pdf] [website] [software]

  • Evan Ray, et al. (2022) Comparing Trained and Untrained Probabilistic Ensemble Forecasts of COVID-19 Cases and Deaths in the United States. Accepted, International Journal of Forecasting [pdf]

  • Andee Kaplan and Jacob Bien (2022) Interactive Exploration of Large Dendrograms with Prototypes. Accepted, The American Statistician [pdf] [software] [code for paper examples]

  • Sangwon Hyun, …, Jacob Bien (2022) Ocean Mover's Distance: Using Optimal Transport for Analyzing Oceanographic Data. Accepted, Proceedings of the Royal Society A [pdf] [code]

  • Sangwon Hyun, Mattias Rolf Cape, Francois Ribalet, and Jacob Bien (2022) Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions. Accepted, Annals of Applied Statistics [pdf] [software]

  • Estee Cramer, et al. (2022) Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the US. Proceedings of the National Academy of Sciences 119(15) [pdf]

  • Daniel McDonald, Jacob Bien, et al. (2021) Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction? Proceedings of the National Academy of Sciences 118(51) [pdf] [supplement] [code to reproduce all results]

  • Jacob Bien, Xiaohan Yan, Léo Simpson, and Christian Müller (2021) Tree-Aggregated Predictive Modeling of Microbiome Data. Scientific Reports 11(14505) [pdf] [software] [code to reproduce all results]

  • Ines Wilms, Sumanta Basu, Jacob Bien, and David Matteson (2021) Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages. Accepted, Journal of the American Statistical Association [pdf] [software]

  • Guo Yu, Daniela Witten, and Jacob Bien (2021) Controlling Costs: Feature Selection on a Budget. Stat [pdf]

  • Xiaohan Yan and Jacob Bien (2020) Rare Feature Selection in High Dimensions. Journal of the American Statistical Association 116(534), 887-900 [pdf] [software] [vignette]

  • William Nicholson, Ines Wilms, Jacob Bien, and David Matteson (2020) High-Dimensional Forecasting via Interpretable Vector Autoregression. Journal of Machine Learning Research 21(166), 1−52. [pdf] [software]

  • Shuxiao Chen and Jacob Bien (2019) Valid Inference Corrected for Outlier Removal. Journal of Computational and Graphical Statistics 29(2), 323-334. [pdf] [software]

  • Guo Yu, Jacob Bien, and Daniela Witten (2019) Discussion of “Covariate-assisted ranking and screening for large-scale two‐sample inference” Journal of the Royal Statistical Society, Series B. 81(2): 229-231. [paper] [supplement]

  • Guo Yu and Jacob Bien (2018) Estimating the Error Variance in a High-Dimensional Linear Model. Biometrika. [pdf] [journal] [software] [vignette]

  • Jacob Bien (2018) Graph-Guided Banding of the Covariance Matrix. Journal of the American Statistical Association. 114(526) 782-792. [pdf] [software]

  • Jacob Bien, Irina Gaynanova, Johannes Lederer, and Christian Müller (2018) Prediction Error Bounds for Linear Regression With the TREX. TEST. 28, 451–474. [pdf]

  • Xiaohan Yan and Jacob Bien (2017) Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations. Statistical Science. 32(4), 531-560. [pdf] [software]

  • Jacob Bien, Irina Gaynanova, Johannes Lederer, and Christian Müller (2016) Non-convex Global Minimization and False Discovery Rate Control for the TREX. Journal of Computational and Graphical Statistics. 27(1), 23-33. [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 Statistical 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]