home
papers
software
people
teaching
misc

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

  • Ameer Dharamshi, Anna Neufeld, Keshav Motwani, Lucy L. Gao, Daniela Witten, Jacob Bien (2023) Generalized Data Thinning Using Sufficient Statistics [pdf]

  • Arkajyoti Saha, Daniela Witten, and Jacob Bien (2022) Inferring Independent Sets of Gaussian Variables After Thresholding Correlations. [pdf] [software]

  • Adel Javanmard, Simeng Shao, and Jacob Bien (2022) Prediction Sets for High-Dimensional Mixture of Experts Models. [pdf]

  • 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 (2023) Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions. Annals of Applied Statistics 17(1), 357-377. [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]