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[PAST EVENT] Colloquium talk: Graph Structure as A Double-Edged Sword in Machine Learning

February 7, 2022
9am
Location
McGlothlin-Street Hall, Online on Zoom
251 Jamestown Rd
Williamsburg, VA 23185Map this location

Abstract: 

Graph structures describing relationships and interactions among

entities are everywhere, ranging from chemical molecule structures in

??microworld, societal networks in daily life to the Internet to

defender-adversary interactions in national security. The ubiquity of

such a graph-structured description of our world calls for effective and

trustworthy methods that make use of and learn to understand information

represented in this structured form. On one hand, graph structure brings

relational inductive bias to facilitate learning algorithms; on the

other hand, such structure could be used in an undesired way threatening

the trustworthiness of machine learning.


In this talk, I will introduce my work on computational questions

pertaining to the double-edged role of graph structure in machine

learning. In particular, I will mainly illustrate the opportunities and

the trustworthiness issues through three threads of my research: 1) how

to utilize graph structure to enhance machine learning when label

information is limited; 2) how to mitigate societal discrimination in

predictions resulting from structural bias; 3) how to understand the

robustness of structure when it can be altered by an adversary. I will

demonstrate my studies on these questions lying at the intersection of

machine learning and graph theory.


Bio:

Lu Lin is a Ph.D. candidate in the Computer Science Department at the

University of Virginia (UVa), advised by Professor Hongning Wang. Before

coming to UVa, she obtained her B.S. and M.S. degree in Computer Science

Department from Beihang University. Her research interests include

artificial intelligence, data mining and applied machine learning on

graph-structured data. She focuses on utilizing graph structure to

advance machine learning in a trustworthy and responsible manner. Her

research has been published at high-impact venues (e.g., KDD, WWW,

AISTATS, WSDM, TKDE).

Contact

Pieter Peers