This talk will introduce the model of “learning-augmented mechanism design” (or “mechanism design with predictions”), which is an alternative model for the design and analysis of mechanisms in strategic settings. Aiming to complement the traditional approach in computer science, which analyzes the performance of algorithms based on worst-case instances, recent work on “algorithms with predictions” has developed algorithms that are enhanced with machine-learned predictions regarding the optimal solution. The algorithms can use this information to guide their decisions and the goal is to achieve much stronger performance guarantees when these predictions are accurate (consistency), while also maintaining good worst-case guarantees, even if these predictions are very inaccurate (robustness). So far, most of these results have been limited to online algorithms but some very recent work has shown that a possibly even more fertile ground for this model is in mechanism design. This talk will cover the foundations of learning-augmented mechanism design and some recent results in this model.
About the speaker
Vasilis Gkatzelis (https://www.cs.drexel.edu/~gkatz/) is an associate professor in computer science at Drexel University. He is a recipient of the NSF Faculty Early Career Development Program (CAREER) award. He previously held positions as a postdoctoral scholar at the computer science departments of UC Berkeley and Stanford University, and as a research fellow at the Simons Institute for the Theory of Computing. He received his PhD from the Courant Institute of New York University and his research focuses on problems in algorithmic game theory and approximation algorithms