Learning Without Training

TL;DR

This dissertation explores mathematical theories for machine learning, proposing new methods for supervised, transfer, and active learning that address theoretical limitations and improve efficiency and accuracy.

Contribution

It introduces a novel supervised learning method, studies transfer learning on partial data, and unifies signal separation with classification for active learning.

Findings

Proposed a method that remedies theoretical shortcomings in supervised learning.

Analyzed the relationship between local smoothness and function lifting in transfer learning.

Developed a fast, accurate active learning algorithm based on signal separation techniques.

Abstract

Machine learning is at the heart of managing the real-world problems associated with massive data. With the success of neural networks on such large-scale problems, more research in machine learning is being conducted now than ever before. This dissertation focuses on three different projects rooted in mathematical theory for machine learning applications. The first project deals with supervised learning and manifold learning. In theory, one of the main problems in supervised learning is that of function approximation: that is, given some data set $\mathcal{D}=\{(x_j,f(x_j))\}_{j=1}^M$, can one build a model $F\approx f$? We introduce a method which aims to remedy several of the theoretical shortcomings of the current paradigm for supervised learning. The second project deals with transfer learning, which is the study of how an approximation process or model learned on one domain can be leveraged to improve the approximation on another domain. We study such liftings of functions when the data is assumed to be known only on a part of the whole domain. We are interested in determining subsets of the target data space on which the lifting can be defined, and how the local smoothness of the function and its lifting are related. The third project is concerned with the classification task in machine learning, particularly in the active learning paradigm. Classification has often been treated as an approximation problem as well, but we propose an alternative approach leveraging techniques originally introduced for signal separation problems. We introduce theory to unify signal separation with classification and a new algorithm which yields competitive accuracy to other recent active learning algorithms while providing results much faster.