Document Type
Lecture
Publication Date
11-8-2019
Abstract
Inference (aka predictive modeling) is in the core of many data science problems. Traditional ap-proaches could be either statistically or computationally efficient, however not necessarily both. The existing principles in deriving these models – such as the maximal likelihood estimation prin-ciple - may have been developed decades ago, and do not take into account the new aspects of the data, such as their large volume, variety, velocity and veracity. On the other hand, many existing empirical algorithms are doing extremely well in a wide spectrum of applications, such as the deep learning framework; however they do not have the theoretical guarantee like these classical meth-ods. We aim to develop new algorithms that are both computationally efficient and statistically optimal. Such a work is fundamental in nature, however will have significant impacts in all data science problems that one may encounter in the society. Following the aforementioned spirit, I will describe a set of my past and current projects including L1-based relaxation, fast nonlinear corre-lation, optimality of detectability, and nonconvex regularization. All of them integrates statistical and computational considerations to develop data analysis tools.
Relational Format
presentation
Recommended Citation
Huo, Xiaoming, "Interface of statistics, computing, and data science" (2019). Colloquium. 1.
https://egrove.olemiss.edu/math_colloquium/1
