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

PhD Thesis Defence



Mr. Yao MING


With the recent advancements of machine learning, especially deep 
learning, we have seen fast-growing applications of these intelligent 
systems in various domains. However, the increasing complexity of these 
systems makes it very challenging to explain or interpret their reasoning 
process, which limits their adoption in critical decision-making 
scenarios. In the meantime, visualization has been effectively applied to 
support the understanding and analyzing of complex systems and large data 
collections. In this thesis, we study how to make machine learning systems 
explainable for human users using visualizations.

We first propose a user-model interaction framework to describe and 
categorize the explainable machine learning problem. Then we discuss the 
role of visualization in explainable machine learning, including How, 
Where, and Why visualization could be used to help explain What parts of 
the machine learning pipeline to Whom. We also summarize the recent 
research advances in this field.

We then grounded our study of different aspects of the explainable problem 
on specific applications: 1) how can visualization help explain the inner 
working mechanisms of deep learning models for model developers and 
researchers? 2) how can we explain the behavior of a model for non-expert 
users with little knowledge in machine learning? 3) how can explainability 
help expert users in various application domains to incorporate domain 
knowledge into the model? We experiment these ideas under a 
human-in-the-loop setting and include preliminary evaluation results in 
this thesis. At last, we discuss our ongoing and future research as well 
as open questions in visualization for explainable machine learning.

Date:			Thursday, 19 December 2019

Time:			10:00am - 12:00noon

Venue:			Room 2132C
 			Lift 19

Chairman:		Prof. Andrew POON (ECE)

Committee Members:	Prof. Huamin QU (Supervisor)
 			Prof. James KWOK
 			Prof. Ke YI
 			Prof. Fugee TSUNG (IEDA)
 			Prof. Victor LI (HKU)

**** ALL are Welcome ****