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08/02/2023
Semi-supervised Cooperative Learning for Multiomics Data Fusion
Multiomics data fusion integrates diverse data modalities, ranging from ...
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12/10/2022
Weakest link pruning of a dendrogram
Hierarchical clustering is a popular method for identifying distinct gro...
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08/21/2022
FastCPH: Efficient Survival Analysis for Neural Networks
The Cox proportional hazards model is a canonical method in survival ana...
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05/04/2022
Estimation of prediction error with known covariate shift
In supervised learning, the estimation of prediction error on unlabeled ...
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02/20/2022
Smooth multi-period forecasting with application to prediction of COVID-19 cases
Forecasting methodologies have always attracted a lot of attention and h...
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01/31/2022
Causal Inference with Orthogonalized Regression Adjustment: Taming the Phantom
Standard regression adjustment gives inconsistent estimates of causal ef...
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01/26/2022
Confidence Intervals for the Generalisation Error of Random Forests
Out-of-bag error is commonly used as an estimate of generalisation error...
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01/26/2022
Confidence intervals for the Cox model test error from cross-validation
Cross-validation (CV) is one of the most widely used techniques in stati...
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12/23/2021
Cooperative learning for multi-view analysis
We propose a new method for supervised learning with multiple sets of fe...
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07/28/2021
Survival stacking: casting survival analysis as a classification problem
While there are many well-developed data science methods for classificat...
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04/01/2021
Cross-validation: what does it estimate and how well does it do it?
Cross-validation is a widely-used technique to estimate prediction error...
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06/02/2020
Feature-weighted elastic net: using "features of features" for better prediction
In some supervised learning settings, the practitioner might have additi...
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03/09/2020
Assessment of Heterogeneous Treatment Effect Estimation Accuracy via Matching
We study the assessment of the accuracy of heterogeneous treatment effec...
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02/28/2020
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial ...
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12/04/2019
Reluctant additive modeling
Sparse generalized additive models (GAMs) are an extension of sparse gen...
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12/04/2019
Reluctant generalized additive modeling
Sparse generalized additive models (GAMs) are an extension of sparse gen...
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11/21/2019
On the Use of C-index for Stratified and Cross-Validated Cox Model
We develop a baseline-adjusted C-index to evaluate fitted Cox proportion...
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09/24/2019
Survival analysis as a classification problem
In this paper, we explore a method for treating survival analysis as a c...
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07/29/2019
A neural network with feature sparsity
We propose a neural network model, with a separate linear (residual) ter...
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07/03/2019
Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
Nonlinear dimensionality reduction methods are a popular tool for data s...
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10/10/2018
Principal component-guided sparse regression
We propose a new method for supervised learning, especially suited to wi...
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07/16/2018
A latent factor approach for prediction from multiple assays
In many domains such as healthcare or finance, data often come in differ...
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06/26/2018
Main effects and interactions in mixed and incomplete data frames
A mixed data frame (MDF) is a table collecting categorical, numerical an...
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06/04/2018
Post model-fitting exploration via a "Next-Door" analysis
We propose a simple method for evaluating the model that has been chosen...
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01/27/2018
More powerful post-selection inference, with application to the Lasso
Investigators often use the data to generate interesting hypotheses and ...
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12/01/2017
A Pliable Lasso
We propose a generalization of the lasso that allows the model coefficie...
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10/31/2017
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Many decisions in healthcare, business, and other policy domains are mad...
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07/27/2017
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
In exciting new work, Bertsimas et al. (2016) showed that the classical ...
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07/01/2017
Some methods for heterogeneous treatment effect estimation in high-dimensions
When devising a course of treatment for a patient, doctors often have li...
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06/30/2017
Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball
We propose the nuclear norm penalty as an alternative to the ridge penal...
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05/30/2017
Sparse canonical correlation analysis
Canonical correlation analysis was proposed by Hotelling [6] and it meas...
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07/22/2016
High-dimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experi...
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12/08/2015
Selective Sequential Model Selection
Many model selection algorithms produce a path of fits specifying a sequ...
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05/26/2014
An Ordered Lasso and Sparse Time-Lagged Regression
We consider regression scenarios where it is natural to impose an order ...
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01/22/2014
Collaborative Regression
We consider the scenario where one observes an outcome variable and sets...
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11/18/2013
A Component Lasso
We propose a new sparse regression method called the component lasso, ba...
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06/27/2012
A Permutation Approach to Testing Interactions in Many Dimensions
To date, testing interactions in high dimensions has been a challenging ...
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05/22/2012
A lasso for hierarchical interactions
We add a set of convex constraints to the lasso to produce sparse intera...
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11/09/2010
