Advances in Deep Learning for Auction and Matching Market Design

Also on Zoom - https://umd.zoom.us/j/97114322433?pwd=TWw0OG8yV3ZTc1d2V0RlYXB6RkNWQT09 The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectrum allocation, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson's 1981 work characterizing single-item optimal auctions, there has been limited progress outside of restricted settings. A recent paper by Dütting et al. circumvents analytic difficulties by applying deep learning techniques to, instead, approximate optimal auctions. Their RegretNet architecture can represent auctions with arbitrary numbers of items and participants; it is trained to be empirically strategyproof, but the property is never exactly verified leaving potential loopholes for market participants to exploit. In parallel, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. Inspired by these advances, in this talk, we discuss extensions of these techniques for approximating auctions using deep learning to address concerns of* fairness while maintaining high revenue and strong incentive guarantees, including learning fairness from human preferences;* certified robustness, that is, verification of claimed strategyproofness of deep learned auctions; and* expressiveness via different demand functions and other constraints.To enable that last point, we propose a new architecture to learn incentive compatible, revenue-maximizing auctions from sampled valuations, which uses the Sinkhorn algorithm to perform a differentiable bipartite matching. Our new framework allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. This talk connects work in the deep learning for auction design space into the deep learning for matching market design space, and provides concrete steps forward regarding differentiable economics and matching market design.This talk covers hot-off-the-presses work led by: PhD students Michael Curry, Ping-yeh Chiang, and Samuel Dooley; and undergraduate students Elizabeth Horishny, Kevin Kuo, Uro Lyi, Anthony Ostuni, and Neehar Peri. Papers have appeared at AI/ML conferences or are currently under review; please check arXiv or get in touch for drafts.