AI Seminar 2024: Learning-augmented algorithms for online optimization and beyond, Nico Christianson

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

Abstract:
Modern AI and ML algorithms can deliver transformative performance improvements for decision-making under uncertainty, where traditional, worst-case algorithms are often too conservative. However, AI and ML lack worst-case guarantees, hindering their deployment to real-world settings like energy systems where safety and reliability are critical. In this talk, I will discuss work on developing machine-learning augmented algorithms with provable performance guarantees, focusing in particular on algorithms and lower bounds for integrating black-box ML “advice” in general online optimization problems. I will also highlight some recent steps toward leveraging uncertainty quantification to improve learning-augmented algorithm performance, as well as applications of our work to energy and sustainable computing.

Presenter Bio:
Nico Christianson is a PhD candidate in Computing and Mathematical Sciences at Caltech, where he is advised by Adam Wierman and Steven Low. Before Caltech, he received his AB in Applied Mathematics from Harvard. He is broadly interested in online algorithms, learning, and optimization, with an emphasis on developing learning-augmented algorithms with provable guarantees for problems spanning energy, carbon-aware computing, and sustainability. Nico’s work is supported by an NSF Graduate Research Fellowship.