Abstract:

The talk kicks off with a simple example, based on a 1975 photo taken in front of Penn State's main administration building, leading to a problem in unsupervised model-based clustering. This example illustrates how an EM algorithm works, which we'll exploit later in extending the methodology to multivariate components that are not assumed to come from any parametric family except as long as they satisfy a property called conditional independence. The first part of the talk ends with an extension of this method involving a technique known as Independent Component Analysis (ICA) and some related open questions. In the second part of the talk, we revisit a problem in supervised clustering that is related to a case currently before the United States Supreme Court. In the late 1990s, a graduate student in statistics at the University of Michigan created a formula to help the undergraduate admissions office classify applicants. This formula used logistic regression and might be considered simplistic by modern statistical standards. Yet the debate that ensued, which centered on the relative sizes of the regression coefficients themselves and culminated in the legal case known as Gratz v. Bollinger, suggests that science and society do not necessarily benefit most from machine learning models that achieve the best possible predictive performance.

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Topic: Colloquium -- Department of Statistics and Actuarial Science -- University of Iowa
Time: Nov 17, 2022 03:15 PM Central Time (US and Canada)

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Meeting ID: 919 6962 2615
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Meeting ID: 919 6962 2615