A Truly Unbiased Model

Any dataset has bias, as the data collection will be intervened by human bias and affected by the nature’s biased law. Therefore, any machine learning model based on it will be biased and limited in generalization. Of course, we have a plenty of “debiasing” techniques that deliberately remove the bias of the “majority”, so as to take care of the “minority” — fairness is believed to be achieved. However, such “taking care of minority” per se is a bias unfair to “majority”. In this talk, I will share a “best of two worlds” model that is truly unbiased. Its design is simple, yet effective and theoretically grounded.

Speaker Details

Hanwang Zhang is an Assistant Professor at Nanyang Technological University’s School of Computer Science and Engineering. His research interests include Computer Vision, Natural Language Processing, Causal Inference, and their combinations. His work has received numerous awards including the IEEE AI’s-10-To-Watch 2020, TMM Prize Paper Award 2020, Alibaba Innovative Research Award 2019, ACM ToMM Best Paper Award 2018, Nanyang Assistant Professorship 2018, ACM SIGIR Best Paper Honorable Mention Award 2016, and ACM MM Best Student Paper Award 2012. Hanwang and his team work actively in causal inference for connecting vision and language. For example, their scene graph detection benchmark won the IEEE CVPR Best Paper Finalist 2019 and their visual dialog agent won the 1st place in Visual Dialog Challenge 2019 and 2nd place in 2018/2020.

Series:
Microsoft Vision+Language Summer Talk Series
Date:
Speakers:
Hanwang Zhang
Affiliation:
Nanyang Technological University's School of Computer Science and Engineering

Series: Microsoft Vision+Language Summer Talk Series