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Beyond Static Classification: Long-term Fairness for Minority Groups via Performative Prediction and Distributionally Robust Optimization
DownloadFall 2022
In recent years machine learning (ML) models have begun to be deployed at enormous scales, but too often without adequate concern for whether or not an ML model will make fair decisions. Fairness in ML is a burgeoning research area, but work to define formal fairness criteria has some serious...
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Fall 2019
High dimensional classification has drawn massive attention due to its increasing application in genetic diagnosis, image or speech recognition and financial analysis. Traditional methods such as Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), which are optimal Bayes...
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Fall 2022
Convolution Neural Networks (CNNs) have rapidly evolved since their neuroscience beginnings. These models efficiently and accurately classify images by optimizing the model’s hidden representations to these images through training. These representa- tions have been shown to resemble neural data...
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Fall 2023
Many standard approaches for conducting statistical inference on regression parameters rely heavily on parametric assumptions and asymptotic results. The wild bootstrap (Mammen, 1993) was developed as a nonparametric means to estimate a sampling distribution and is particularly useful when...
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Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures: Unsupervised Learning with Outlier Identification for Asymmetric Clusters in High Dimensions
DownloadFall 2021
A family of parsimonious contaminated shifted asymmetric Laplace mixtures is developed for asymmetric clusters in the presence of outliers and noise (referred to as bad points herein). A series of constraints are applied to a modified factor analyzer structure of the scale matrix parameters,...