Towards Automated and Trustworthy Machine Learning

Speaker:        Dr. Minhao Cheng
                University of California, Los Angeles

Title:          "Towards Automated and Trustworthy Machine Learning"

Date:           Tuesday, 20 April 2021

Time:           10:00 am - 11:00 am

Zoom Link:
https://hkust.zoom.us/j/465698645?pwd=c2E4VTE3b2lEYnBXcyt4VXJITXRIdz09

Meeting ID:     465 698 645
Passcode:       20202021


Abstract:

Deep neural networks have achieved unprecedented success over a variety of 
tasks and across different domains.  At the same time, it has been shown 
that DNNs models are vulnerable to a very small human-imperceptible 
perturbation. In this talk, I will first introduce how to develop a 
query-efficient framework to generate such perturbation which could apply 
to industrial-strength image classifiers in real-world scenarios. 
Furthermore, I will show a single query oracle for retrieving signs of 
directional derivative could be utilized to largely improve the query 
efficiency.  Lastly, to develop an accurate and trustworthy model, I will 
talk about how to automate the manual process of architecture design in a 
differentiable manner and improve its limitation on the architecture 
selection.


***************************
Biography:

Minhao Cheng obtained his Ph.D. degree in the Department of Computer 
Science from the University of California, Los Angeles under the 
supervision of Prof. Cho-Jui Hsieh. His research focus is broadly on 
machine learning with a focus on machine learning robustness and AutoML. 
He has published over 15 papers on top-tier AI conferences including ICML, 
NeurIPS, ICLR, ACL, AAAI, etc. He is a recipient of the ICLR 2021 
Outstanding Paper Award.