Abstract:

Over the last decade, machine learning methods, notably neural networks, have played a key role in enabling major progress in artificial intelligence and its applications. Unfortunately, despite their excellent performance in many use cases, neural networks have been shown to be sensitive to input perturbations, including adversarial attacks. In this presentation, I will give an introduction to neural network robustness and explain how work in this area effectively combines AI methods from machine learning, optimisation and automated reasoning. I will explain the concept of robustness verification and introduce local robustness distributions, which afford a rigorous and nuanced assessment of neural network robustness against input perturbations. I will give examples from image and audio classification and briefly discuss connections to questions of bias and fairness, as well as the way in which this line of work relates to the broader effort of my group towards safe, dependable and sustainable AI.

Bio:

Holger H. Hoos holds an Alexander von Humboldt professorship in AI at RWTH Aachen University (Germany), where he also leads the AI Center, as well as a professorship in machine learning at Universiteit Leiden (the Netherlands) and an affiliate professorship in computer science at the University of British Columbia (Canada). He is a Fellow of the Association of Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI), and he currently serves as president of the European AI Association (EurAI); he is also past president of the Canadian Association for Artificial Intelligence and one of initiators of CAIRNE (formerly CLAIRE), an initiative by the European AI community that seeks to strengthen European excellence in AI research and innovation (cairne.eu).
 

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