Lower the Barrier of Machine Learning: Meta Learning for Transfer Learning and AutoML

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Lower the Barrier of Machine Learning: Meta Learning for Transfer 
Learning and AutoML"

By

Mr. Wenyuan DAI


Abstract

Recent years, machine learning becomes the main methodology to develop 
artificial intelligence technology, due to the emerge of big data. 
However, traditional machine learning may face to three barriers: lack of 
data, poor feature quality, and less data scientists. In this thesis, we 
focus on how to lower the barrier of machine learning. We propose to use 
meta learning methodology to solve these problems. Specifically, meta 
learning can be applied to solve the transfer learning and AutoML 
problems. Transfer learning can be used to weaken the impact of small data 
and poor feature problems, and AutoML can be used to solve the problem 
that there are not enough data scientists and then we may use normal 
engineers to build up AI systems. As the result, the three main barriers 
have been lowered correspondingly. We designed several new algorithms to 
solve the data, feature and model tuning problems, and showed advantages 
on many empirical studies.

As meta learning may rely on auxiliary data from other sources, we found 
that it may lead to privacy problem. To solve this problem and make meta 
learning better applied, we design a new privacy-preserving learning 
algorithm. In this algorithm, we show how to learn from data without 
accessing any privacy information.


Date:			Friday, 15 November 2019

Time:			3:00pm - 5:00pm

Venue:			Room 2610
 			Lifts 31/32

Chairman:		Prof. Bing-yi JING (MATH)

Committee Members:	Prof. Qiang YANG (Supervisor)
 			Prof. Kai CHEN
 			Prof. Qifeng CHEN
 			Prof. Yuan YAO (MATH)
 			Prof. Kuo Chin Irwin KING (CUHK)


**** ALL are Welcome ****