b'Jacob Bien'

research

∙ 05/30/2023

Predicting Rare Events by Shrinking Towards Proportional Odds

Training classifiers is difficult with severe class imbalance, but many ...

0 Gregory Faletto, et al. ∙

research

∙ 03/22/2023

Generalized Data Thinning Using Sufficient Statistics

Our goal is to develop a general strategy to decompose a random variable...

0 Ameer Dharamshi, et al. ∙

research

∙ 11/02/2022

Inferring independent sets of Gaussian variables after thresholding correlations

We consider testing whether a set of Gaussian variables, selected from t...

0 Arkajyoti Saha, et al. ∙

research

∙ 10/30/2022

Prediction Sets for High-Dimensional Mixture of Experts Models

Large datasets make it possible to build predictive models that can capt...

0 Adel Javanmard, et al. ∙

research

∙ 06/03/2022

Interactive Exploration of Large Dendrograms with Prototypes

Hierarchical clustering is one of the standard methods taught for identi...

0 Andee Kaplan, et al. ∙

research

∙ 01/28/2022

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term bu...

0 Evan L. Ray, et al. ∙

research

∙ 01/03/2022

Cluster Stability Selection

Stability selection (Meinshausen and Buhlmann, 2010) makes any feature s...

0 Gregory Faletto, et al. ∙

research

∙ 11/16/2021

Ocean Mover's Distance: Using Optimal Transport for Analyzing Oceanographic Data

Modern ocean datasets are large, multi-dimensional, and inherently spati...

0 Sangwon Hyun, et al. ∙

research

∙ 08/11/2021

Controlling the False Split Rate in Tree-Based Aggregation

In many domains, data measurements can naturally be associated with the ...

0 Simeng Shao, et al. ∙

research

∙ 01/29/2021

Tree-based Node Aggregation in Sparse Graphical Models

High-dimensional graphical models are often estimated using regularizati...

0 Ines Wilms, et al. ∙

research

∙ 12/05/2020

Selective Inference for Hierarchical Clustering

Testing for a difference in means between two groups is fundamental to a...

0 Lucy L. Gao, et al. ∙

research

∙ 08/25/2020

Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions

The ocean is filled with microscopic microalgae called phytoplankton, wh...

0 Sangwon Hyun, et al. ∙

research

∙ 10/08/2019

Controlling Costs: Feature Selection on a Budget

The traditional framework for feature selection treats all features as c...

0 Guo Yu, et al. ∙

research

∙ 09/25/2019

Testing for Association in Multi-View Network Data

In this paper, we consider data consisting of multiple networks, each co...

0 Lucy L. Gao, et al. ∙

research

∙ 07/19/2019

Reluctant Interaction Modeling

Including pairwise interactions between the predictors of a regression m...

0 Guo Yu, et al. ∙

research

∙ 01/12/2019

Are Clusterings of Multiple Data Views Independent?

In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), mul...

0 Lucy L. Gao, et al. ∙

research

∙ 03/18/2018

Rare Feature Selection in High Dimensions

It is common in modern prediction problems for many predictor variables ...

0 Xiaohan Yan, et al. ∙

research

∙ 01/04/2018

Prediction Error Bounds for Linear Regression With the TREX

The TREX is a recently introduced approach to sparse linear regression. ...

0 Jacob Bien, et al. ∙

research

∙ 12/06/2017

Estimating the error variance in a high-dimensional linear model

The lasso has been studied extensively as a tool for estimating the coef...

0 Guo Yu, et al. ∙

research

∙ 11/29/2017

Valid Inference Corrected for Outlier Removal

Ordinary least square (OLS) estimation of a linear regression model is w...

0 Shuxiao Chen, et al. ∙

research

∙ 11/09/2017

Interpretable Vector AutoRegressions with Exogenous Time Series

The Vector AutoRegressive (VAR) model is fundamental to the study of mul...

0 Ines Wilms, et al. ∙

research

∙ 06/30/2016

The Simulator: An Engine to Streamline Simulations

The simulator is an R package that streamlines the process of performing...

0 Jacob Bien, et al. ∙

research

∙ 06/01/2016

Graph-Guided Banding of the Covariance Matrix

Regularization has become a primary tool for developing reliable estimat...

0 Jacob Bien, et al. ∙

research

∙ 04/25/2016

Learning Local Dependence In Ordered Data

In many applications, data come with a natural ordering. This ordering c...

0 Guo Yu, et al. ∙

research

∙ 04/22/2016

Non-convex Global Minimization and False Discovery Rate Control for the TREX

The TREX is a recently introduced method for performing sparse high-dime...

0 Jacob Bien, et al. ∙

research

∙ 12/05/2015

Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations

Demanding sparsity in estimated models has become a routine practice in ...

0 Xiaohan Yan, et al. ∙

research

∙ 12/17/2014

Hierarchical Vector Autoregression

Vector autoregression (VAR) is a fundamental tool for modeling the joint...

0 William B. Nicholson, et al. ∙

research

∙ 07/17/2014

Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible an...

0 Yin Lou, et al. ∙

research

∙ 05/23/2014

Convex Banding of the Covariance Matrix

We introduce a new sparse estimator of the covariance matrix for high-di...

0 Jacob Bien, et al. ∙

research

∙ 05/22/2012

A lasso for hierarchical interactions

We add a set of convex constraints to the lasso to produce sparse intera...

0 Jacob Bien, et al. ∙

research

∙ 11/09/2010

Strong rules for discarding predictors in lasso-type problems

We consider rules for discarding predictors in lasso regression and rela...

0 Robert Tibshirani, et al. ∙

research

∙ 11/01/2010

CUR from a Sparse Optimization Viewpoint

The CUR decomposition provides an approximation of a matrix X that has l...

0 Jacob Bien, et al. ∙

Jacob Bien

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