DART Monthly Webinar >> Towards Robust Machine Learning under Distribution Shift and Adversarial Attack, presented by Xintao Wu

By DART

Wednesday, October 27, 2021 1:30pm to 2:30pm

Abstract

As big data and AI technologies are deployed to make critical decisions that potentially affect individuals (e.g., employment, college admissions, credit, and health insurance), there are increasing concerns from the public on privacy, fairness, safety, and robustness issues of data analytics, collection, sharing and decision making. In this talk, we first overview our social awareness research, in particular, on how to mitigate side effect of enforcing one social concern on another, and how to address multiple social concerns simultaneously. We then focus on robustness of machine learning under two representative scenarios, distribution shift and adversarial attack. In the former scenario, we present robust learning based on kernel reweighing and Heckman model. In the second scenario, we present adaptive defense that purposely leverages multiple types of adversarial samples to learn the context information in the training. We conclude the talk with some future research directions.

 

Presenter Bio

Dr. Xintao Wu is the professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at the University of Arkansas. In the NSF DART project, he co-leads the social awareness thrust. He was a faculty member in College of Computing and Informatics at the University of North Carolina at Charlotte from 2001 to 2014. He got his BS degree in Information Science from the University of Science and Technology of China in 1994, ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and Ph.D. in Information Technology from George Mason University in 2001. Dr. Wu's major research interests include data mining, privacy and security, fairness aware learning, and big data analysis.  Dr. Wu has published over 150 scholarly papers and served on editorial boards of several international journals and many program committees of top international conferences in data mining and AI. Dr. Wu is also a recipient of NSF CAREER Award (2006) and several paper awards including PAKDD'13 Best Application Paper Award, BIBM'13 Best Paper Award, CNS'19 Best Paper Award, and PAKDD'19 Most Influential Paper Award.

 

About the DART Monthly Webinar Series

The DART Monthly Webinar Series provides an opportunity for DART project faculty and students to share short presentations about their work followed by open discussion and questions. Webinar topics will rotate between project management items like reporting and overviews of DART as a project, as well as specific research topics, important research results, and more.

 

About DART

Data Analytics that are Robust and Trusted, or DART for short, is a 5-year, $20 million research initiative funded by the National Science Foundation that aims to (1) systematically investigate key aspects of barriers to practical application and acceptance of data analytics and (2) develop novel, integrated solutions to address them.

 

DART operates as a true, multi-institution, multi-disciplinary data science research center, in which faculty and students from campuses across the state work together on targeted problems important to the research community and the economy of Arkansas.

Learn more about DART at dartproject.org.

 

Webinar FAQs:

-- Webinars will be hosted using the University of Arkansas Zoom account which requires that you have a Zoom account to join (free accounts are available https://zoom.us/freesignup/).

-- All webinars will be recorded and made available after presentation. Links will be posted on this site.

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This material is based upon work supported by the National Science Foundation under Award No. OIA-1946391. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

 

Event Details

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