Assessing Variable Importance Nonparametrically using Machine Learning Techniques
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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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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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Contributed talk at the Thirty-fifth International Conference on Machine Learning.
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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.
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Invited talk on my dissertation research given at the UW Biostatistics Colloquium.
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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.
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Contributed talk at the 27th International Dynamics and Evolution of HIV and Other Human Viruses Meeting on SLAPNAP (see publications).
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We discuss our paper to be published in the Proceedings of the Thirty-seventh International Conference on Machine Learning.
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I discuss a framework for inference on general model-agnostic variable importance measures.
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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.
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I discuss three approaches towards a more principled use of machine learning: inference on the goodness of fit, inference on variable importance, and containerization.
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I discuss a framework for inference on general model-agnostic variable importance measures.
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Keynote talk at the 3rd annual Hutch United Symposium.
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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.
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I discuss a framework for inference on general model-agnostic variable importance measures.
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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.
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I discuss a method for model-agnostic variable selection that is robust to model misspecification and valid in settings with missing data.
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I discuss a framework for inference on general model-agnostic variable importance measures and possible summary measures for longitudinal variable importance.