[Defense] Highly Scalable and Accelerated Kernel Machine Training For Classification and Regression Problems
Thursday, November 17, 2022
4:30 pm - 6:00 pm
will defend her dissertation
Highly Scalable and Accelerated Kernel Machine Training For Classification and Regression Problems
Mathematical optimization is the backbone of any machine learning algorithm, data science, and engineering. Kernel machines are a class of machine learning algorithms primarily for classification and regression problems. They are statistically well-founded for linear data such as Support Vector Machine (SVM) and Logistic Regression (LR). However, in the real world, the data often establishes non-linear patterns that are harder to characterize using traditional linear models. A set of positive definite kernel functions were developed to effectively capture and analyze these unknown patterns. Although Kernel machines have demonstrated a solid ability to characterize intricate patterns, scaling Kernel machines for large-scale datasets is prohibitively expensive, even for a cluster of computers. Over the last decade, there have been synergistic advancements in HPC (High Performance Computing) systems, such as distributed memory clusters, many-core systems, accelerators, and emerging technology such as neural engines or tensor core units that present new opportunities and challenges for enabling large-scale Kernel machines. This thesis offers a high-performance software system that is a collection of Kernel machine training algorithms. The algorithms are fast and scalable to large-scale datasets and computing resources. They employ numerical optimization and structured low-rank linear algebra algorithms that are conducive to parallelization and acceleration and pioneer the use of neural engines in numerical linear algebra and optimization.
4:30PM - 6:00PM CT
Online via MS Teams
Dr. Panruo Wu, dissertation advisor
Faculty, students and the general public are invited.