Title: Flexible Supervised Learning for Heterogeneous Data Abstract: Data heterogeneity is a challenging problem in modern data analysis. In particular, many classical statistical methodologies may show inadequate performance on heterogeneous datasets because the key homogeneity assumption fails. In this dissertation, we … Read more
Yuzixuan Zhu Preprocessing and First-Order Primal-Dual Algorithms for Convex Optimization Advisors: Dr. Gabor Pataki and Dr. Quoc Tran-Dinh This talk focuses on two topics in the field of convex optimization: preprocessing algorithms for semidefinite programs (SDPs), and first-order primal-dual … Read more
Xi Yang Machine Learning Methods in HDLSS Settings During the exploration of high dimension-low-sample-size (HDLSS) data in different fields such as genetics, finance, computer science, etc, various machine learning methods have been developed. This dissertation includes the invention of novel … Read more
Mark He Community Detection in Multimodal Networks Community detection on networks is a basic, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks … Read more
Modern data collection in bioinformatics and other big-data paradigms often incorporates traits derived from multiple different points of view of the observations. We call this data multi-view data or multi-block data. The field of data integration develops and applies new … Read more
Sparse Machine Learning Methods for Prediction and Personalized Medicine With growing interest to use black-box machine learning for complex data with many feature variables, it is critical to obtain a prediction model that only depends on a small set of … Read more
Techniques in network embedding and Gaussian comparison for high-dimensional statistics This dissertation consists of research on two high-dimensional statistical problems. In the first part of the dissertation, we study Gaussian comparison which is an important technique for comparing distributions and … Read more
Kevin O’Connor Computation and Consistent Estimation of Stationary Optimal Transport Plans In this dissertation, we study optimal transport (OT) for stationary stochastic processes, a field that we refer to as stationary optimal transport. Through example and theory, we argue that … Read more
Inverse problems for a class of stochastic differential equations in a generalized fiducial setting. Abstract: In this talk, we approach the inverse problem of parameter inference for stochastic ordinary differential equations with constant drift. The data for inference comes not … Read more
NEW STOCHASTIC AND RANDOMIZED ALGORITHMS FOR NONCONVEX OPTIMIZATION IN MACHINE LEARNING The dissertation consists of new stochastic first-order methods to solve nonconvex optimization models which cover many applications in machine learning. Firstly, we propose ProxSARAH, a new framework that … Read more
Title: Block Dense Weighted Networks with Augmented Degree Correction Abstract: Dense networks with weighted connections often exhibit a community like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on … Read more
Title: Clustering and Classification with Feature Selection for High-Dimensional Data Abstract: In this dissertation, we discuss several methods for clustering and classification with feature selection for high-dimensional data. In the first part, we focus on the problem of biclustering, … Read more
