Trustworthy Machine Learning under Social and Adversarial Data Sources

Speaker: Han Shao
         Toyota Technological Institute at Chicago (TTIC)

Title:   "Trustworthy Machine Learning under Social and Adversarial
         Data Sources"

Date:    Wednesday, 21 February 2024

Time:    10:00am - 11:00am

Zoom link:

Meeting ID: 966 8851 6988
Passcode: 202425


Machine learning has witnessed remarkable breakthroughs in recent years.
Most machine learning techniques assume that the training and test data
are sampled from an underlying distribution and aim to find a predictor
with low population loss. However, data may be generated by strategic
individuals, collected by self-interested agents, possibly poisoned by
adversarial attackers, and used to create predictors, models, and policies
satisfying multiple objectives. As a result, predictors may underperform.
To ensure the success of machine learning, it is crucial to develop
trustworthy algorithms capable of handling these factors.


Han Shao is a fifth-year Ph.D. student at Toyota Technological Institute
at Chicago (TTIC), advised by Prof. Avrim Blum. Her research focuses on
theoretical foundations of machine learning, with a specific focus on
fundamental questions arising from human social and adversarial behaviors
in the learning process. She is interested in understanding how these
behaviors affect machine learning systems and developing methods to
enhance accuracy/robustness. Additionally, she is interested in gaining a
theoretical understanding of empirical observations concerning adversarial
robustness. Her papers have been published at machine learning venues
including NeurIPS, ICML, COLT, etc. She was awarded EECS Rising Star by
Georgia Tech and Rising Star in Machine Learning by the University of
Maryland in 2023.