Search Results for author:
Found 26 papers, 12 papers with code
A lasso for hierarchical interactions
We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or both variables are marginally important.
A significance test for the lasso
We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an $\operatorname {Exp}(1)$ asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model).
Statistics Theory Methodology Statistics Theory
A Component Lasso
We propose a new sparse regression method called the component lasso, based on a simple idea.
Exact Post-Selection Inference for Sequential Regression Procedures
We propose new inference tools for forward stepwise regression, least angle regression, and the lasso.
Methodology 62F03, 62G15
Collaborative Regression
We propose a method for performing sparse supervised canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA when one of the datasets is a vector.
An Ordered Lasso and Sparse Time-Lagged Regression
We consider regression scenarios where it is natural to impose an order constraint on the coefficients.
Selective Sequential Model Selection
Extending the selected-model tests of Fithian et al. (2014), we construct p-values for each step in the path which account for the adaptive selection of the model path using the data.
High-dimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect.
Sparse canonical correlation analysis
Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables.
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 penalty for regularized multinomial regression.
Some methods for heterogeneous treatment effect estimation in high-dimensions
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials.
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
In exciting new work, Bertsimas et al. (2016) showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO) problem.
Methodology Computation
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Using simulations, we show that using synth-validation to select a causal inference method for each study lowers the expected estimation error relative to consistently using any single method.
A Pliable Lasso
We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of modifying variables.
Methodology
A comparison of methods for model selection when estimating individual treatment effects
Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.
Principal component-guided sparse regression
We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations.
Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
Nonlinear dimensionality reduction methods are a popular tool for data scientists and researchers to visualize complex, high dimensional data.
LassoNet: A Neural Network with Feature Sparsity
Unlike other approaches to feature selection for neural nets, our method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly.
Reluctant generalized additive modeling
Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable.
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
Feature-weighted elastic net: using "features of features" for better prediction
In some supervised learning settings, the practitioner might have additional information on the features used for prediction.
Cross-validation: what does it estimate and how well does it do it?
Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood.
Cooperative learning for multiview analysis
Leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.
Confidence intervals for the Cox model test error from cross-validation
Cross-validation (CV) is one of the most widely used techniques in statistical learning for estimating the test error of a model, but its behavior is not yet fully understood.
FastCPH: Efficient Survival Analysis for Neural Networks
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.
Semi-supervised Cooperative Learning for Multiomics Data Fusion
Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predictions on outcomes of interest related to disease phenotypes and treatment responses.
