[Defense] Highly Scalable and Accelerated Kernel Machine Training For Classification and Regression Problems
Thursday, November 17, 2022
4:30 pm - 6:00 pm
In
Partial
Fulfillment
of
the
Requirements
for
the
Degree
of
Doctor
of
Philosophy
Ruchi
Shah
will
defend
her
dissertation
Highly
Scalable
and
Accelerated
Kernel
Machine
Training
For
Classification
and
Regression
Problems
Abstract
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.
Thursday,
November
17,
2022
4:30PM
-
6:00PM
CT
Online
via
聽
Dr. Panruo Wu, dissertation advisor
Faculty, students and the general public are invited.
