Huan Zhang, "How Can We Trust a Black-box? A Quest for Scalable and Powerful Neural Network Verifiers"

Event Type

Lecture

Sponsor

Computer Science Department at the University of Illinois, Secure Learning Lab

Virtual

Join online

Date

Sep 23, 2021   3:30 - 4:45 pm  

Speaker

Huan Zhang, Postdoctoral Researcher, Carnegie Mellon University

Contact

Madeleine Garvey

E-Mail

mgarvey@illinois.edu

Views

168

Originating Calendar

Siebel School Speakers Calendar

Abstract: Neural networks have become a crucial element in modern artificial intelligence.
However, they are often black-boxes and can behave unexpectedly and produce surprisingly
wrong results under malicious inputs. When applying neural networks to mission-critical
systems such as autonomous driving and aircraft control, it is often desirable to formally
verify their trustworthiness such as safety and robustness. Unfortunately, the complexity of
neural networks has made the task of formally verifying their properties very challenging. To
tackle this challenge, I first propose an efficient verification algorithm based on linear
relaxations of neural networks, which produces guaranteed output bounds given bounded
input perturbations. The algorithm propagates linear inequalities through the network
efficiently in a backward manner and can be applied to arbitrary network architectures. To
reduce relaxation errors, I develop an efficient optimization procedure that can tighten
verification bounds rapidly on machine learning accelerators such as GPUs. Lastly, I discuss
how to further empower the verifier with branch and bound by incorporating the additional
branching constraints into the bound propagation procedure. The combination of these
advanced neural network verification techniques leads to α,β-CROWN
(alpha-beta-CROWN), a scalable, powerful and GPU-based neural network verifier that won
the 2nd International Verification of Neural Networks Competition (VNN-COMP’21) with the
highest total score.

Biography: Huan Zhang is a postdoctoral researcher at Carnegie Mellon University,
supervised by Prof. Zico Kolter. He received his Ph.D. degree in Computer Science at UCLA
in 2020, advised by Prof. Cho-Jui Hsieh. Huan's research focuses on the robustness and
trustworthiness of artificial intelligence (AI), especially on using formal verification and
provable methods to evaluate and enhance the robustness of machine learning models,
such as deep neural networks and tree ensembles. Huan systematically studied the
robustness of many machine learning scenarios including reinforcement learning, natural
language processing, and image generation. Huan led the development of α,β-CROWN, a
toolbox for neural network robustness verification, which won the first prize in Verification of
Neural Networks Competition (VNN-COMP'21). Huan Zhang was awarded an IBM Ph.D.
fellowship during 2018 - 2020 and the 2021 AdvML Rising Star Award sponsored by
MIT-IBM Watson AI Lab.