Professor Xiaotong Shen

CLA Statistics, School of
College of Liberal Arts
Twin Cities
Project Title: 
Deep Learning Inference

At the heart of data science is an uncertainty quantification, particularly for a complex modeling procedure such as deep neural networks involving tuning. This project will concern a novel data perturbation simulator (DPS) to generate synthetic data to replicate a raw sample in statistical inference, including numerical and unstructured data such as texts. Also, DPS permits an estimation of a sample's distribution or density, thus the sampling distribution of any statistic and its distributional characteristics. On this ground, these researchers are developing a Monte Carlo data perturbation inference framework (MCDP) with a statistical guarantee of its validity. In pivotal inference, MCDP yields a valid conclusion without a reference sample for estimating the data-generating mechanism as if one had performed simulations. In non-pivotal inference, MCDP uses an independent reference sample to separate the distribution estimation from inference, yielding a credible conclusion even with limited data. Finally, the group will concentrate on post-inference, generative inference, and natural language inference to demonstrate its potential as an inference tool for complex problems.

Project Investigators

Yuanhao Cai
Taeyoung Chang
Li Chen
Ruoyu He
Yifei Liu
Jingchen Ren
Professor Xiaotong Shen
Xin-Yu Tian
Minjie Wang
Yuchen Yao
Hongru Zhao
Qiuyun Zhu
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