Assessing Variable Importance Nonparametrically using Machine Learning Techniques
Published:
This talk (on a preliminary version of my R-squared variable importance paper published in Biometrics) was selected as the Most Outstanding Oral Paper.
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portfolio
publications
talks
Nonparametric Variable Importance using an Augmented Neural Network with Multi-Task Learning
Published:
Contributed talk at the Thirty-fifth International Conference on Machine Learning.
Assessing Variable Importance Nonparametrically using Machine Learning Techniques
Published:
This talk (on a preliminary version of my R-squared variable importance paper published in Biometrics) was selected for an ASA Biometrics Section Travel Award.
Assessing Variable Importance Nonparametrically using Machine Learning Techniques
Published:
Invited talk on my dissertation research given at the UW Biostatistics Colloquium.
A Unified Approach to Nonparametric Variable Importance Assessment
Published:
This talk (on a preliminary version of my general variable importance paper published in Journal of the American Statistical Association) was selected for an ASA Nonparametrics Section Travel Award.
Guiding HIV-1 Antibody Regimen Down-selection and Prevention Efficacy Trial Design using Machine Learning
Published:
Contributed talk at the 27th International Dynamics and Evolution of HIV and Other Human Viruses Meeting on SLAPNAP (see publications).
Efficient Nonparametric Statistical Inference on Population Feature Importance using Shapley Values
Published:
We discuss our paper to be published in the Proceedings of the Thirty-seventh International Conference on Machine Learning.
A Unified Approach for Inference for Model-Agnostic Variable Importance
Published:
I discuss a framework for inference on general model-agnostic variable importance measures.
Statistics in Infectious Disease Research
Published:
Guest lecture on some of the ways that statistics is used in infectious disease research in the Computational Biology course at Roanoke Valley Governor’s School.
Statistical Inference and Containerization in Computational Pipelines
Published:
I discuss three approaches towards a more principled use of machine learning: inference on the goodness of fit, inference on variable importance, and containerization.
Inference for Model-Agnostic Variable Importance
Published:
I discuss a framework for inference on general model-agnostic variable importance measures.
Inference for Model-Agnostic Variable Importance
Published:
Keynote talk at the 3rd annual Hutch United Symposium.
Flexible Variable Selection in the Presence of Missing Data
Published:
I gave a talk on work in progress, developing methods for variable selection in settings with missing data that do not rely on (generalized) linear models.
Inference for Model-Agnostic Variable Importance
Published:
I discuss a framework for inference on general model-agnostic variable importance measures.
Model-Agnostic Variable Importance and Selection
Published:
I discuss a framework for inference on general model-agnostic variable importance measures, and how this framework can be used to perform variable selection. I also briefly discuss several directions of current work, including longitudinal variable importance; a measure of how important variables are for tailoring treatment; and fairness-aware variable importance.
Flexible Variable Selection with Missing Data
Published:
I discuss a method for model-agnostic variable selection that is robust to model misspecification and valid in settings with missing data.
Inference for Model-Agnostic Variable Importance
Published:
I discuss a framework for inference on general model-agnostic variable importance measures and possible summary measures for longitudinal variable importance.