AAAI New Faculty Highlights: General and Scalable Optimization for Robust AI
Keywords:New Faculty Highlights
AbstractDeep neural networks (DNNs) can easily be manipulated (by an adversary) to output drastically different predictions and can be done so in a controlled and directed way. This process is known as adversarial attack and is considered one of the major hurdles in using DNNs in high-stakes and real-world applications. Although developing methods to secure DNNs against adversaries is now a primary research focus, it suffers from limitations such as lack of optimization generality and lack of optimization scalability. My research highlights will offer a holistic understanding of optimization foundations for robust AI, peer into their emerging challenges, and present recent solutions developed by my research group.
How to Cite
Liu, S. (2023). AAAI New Faculty Highlights: General and Scalable Optimization for Robust AI. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15447-15447. https://doi.org/10.1609/aaai.v37i13.26814
New Faculty Highlights