Abstract: In this talk, I present a machine learning-powered iterative combinatorial auction (CA). The main goal of integrating machine learning (ML) into the auction is to improve preference elicitation, which is a major challenge in large CAs. In contrast to prior work, this auction design uses value queries instead of prices to drive the elicitation. The ML algorithm is used to help the auction mechanism decide which value queries to ask in every iteration. While using ML inside an auction introduces new challenges, I demonstrate how we obtain an auction design that is individually rational, has good incentives, and is computationally tractable. Via simulations, I benchmark our new auction against the well-known combinatorial clock auction (CCA). The results indicate that the ML-powered auction achieves higher allocative efficiency than the CCA, even with only a small number of value queries.