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Advances in Monte Carlo Variational Inference and Applied Probabilistic Modeling
(2018-05-10)
Galvanized by the accelerated pace and ease of data collection, researchers in more and more disciplines are turning to large, heterogeneous datasets to answer scientific questions. Divining insight from massive and complex ...
Off Policy Reinforcement Learning for Real-World Settings
(2021-07-12)
In this dissertation, we aim to adapt reinforcement learning (RL) to real-world, high-risk settings. We study how to optimize sequential decision-making in complex settings with large observational data repositories where ...
Ending Research Subject Overexploitation: Methods to Reduce Respondent Overuse and Privacy Violations while Increasing Insights from Data
(2022-11-23)
The low price of data collection and use in the Internet age has facilitated collective ir- responsibility, where private companies, academics, and governments all fail to internalize the costs to respondents and other ...
How Sensitive Is Your Data: Sensitivity Analysis for Missing Data and Enhanced Tipping Point Displays for a Simulated 2^2 Factorial Designed Experiment
(2017-07-14)
Missing data is a prevailing issue for statisticians and medical practitioners, who must deal with incomplete information in data from many sources, including clinical trials and other randomized experiments. This issue ...
Aspirin and Alcohol in Relation to Lethal Prostate Cancer
(2018-04-26)
Problem: Prostate cancer is the most commonly diagnosed cancer and second-leading cancer cause of death among U.S. men. However, most prostate cancers are indolent. Thus, it is necessary to identify risk factors for lethal ...
OpenDP Programming Framework for Renyi Privacy Filters and Odometers
(2022-05-23)
Data scientists work with large-scale sensitive data, which inevitably leads to privacy risks. Differential Privacy (DP) is a mathematical definition of privacy that aims to mitigate privacy risks inherent in data analysis ...
Diagnostic Tools in Missing Data and Causal Inference on Time Series
(2018-05-16)
This thesis is divided into two self-contained parts.
The first part focuses on diagnostic tools for missing data. Models for analyzing multivariate data sets with missing values require strong, often unassessable, ...
Contributions to Scalable Bayesian Computation
(2022-05-10)
This manuscript presents four projects related to Bayesian computation in large-scale settings. Each chapter is self-contained, and their respective abstracts are given below.
Chapter 1: Markov chain Monte Carlo (MCMC) ...
Detecting Meaningful Relationships in Large Data Sets
(2018-05-01)
As data sets grow and algorithms scale, two questions have become central to data-rich science. The first is the exploration question: how can we avoid only testing hypotheses consistent with current models and instead ...
Policy and Inequality in the Criminal Legal System
(2022-03-17)
This dissertation contains three chapters exploring the application of law and bureaucratic practices in law enforcement agencies and their impacts on community welfare across race and class. Chapter 1 is an analysis of ...