[CMU AI Seminar] Special! October 26 at 1:30pm (NSH 3305 & Zoom) -- John P. Dickerson (UMD) -- Robustness, Privacy, Fairness, and Credibility? Pushing the Boundaries of Economic Design with Deep Learning -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We invite you to a special installment of our CMU AI Seminar Series* tomorrow,
this Wednesday (10/26)* from *1:30 - 2:30** PM (U.S. Eastern time)*,
sponsored by SambaNova Systems <https://sambanova.ai/>. The seminar will be
held in NSH 3305 *with pizza provided *and will be streamed on Zoom.

To learn more about the seminar series or to see the future schedule,
please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

Tomorrow (10/26), *John P. Dickerson* (UMD) will be giving a talk
titled *"**Robustness,
Privacy, Fairness, and Credibility? Pushing the Boundaries of Economic
Design with Deep Learning**".*

*Title*: Robustness, Privacy, Fairness, and Credibility? Pushing the
Boundaries of Economic Design with Deep Learning

*Talk Abstract*: 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.

*Speaker Bio: *John P Dickerson is co-founder and Chief Scientist of
Arthur, the AI performance monitoring company, as well as Associate
Professor of Computer Science at the University of Maryland.  He is a
recipient of awards such as the NSF CAREER Award, IEEE Intelligent Systems
AI's 10 to Watch, Google Faculty Research Award, Google AI for Social Good
Award, and paper awards and nominations at venues such as AAAI.  His
research centers on solving practical economic problems using techniques
from computer science, stochastic optimization, and machine learning. He
has worked extensively on theoretical and empirical approaches to organ
exchange where his work has set policy at the UNOS nationwide kidney
exchange; worldwide blood donation markets with Facebook; game-theoretic
approaches to counter-terrorism and negotiation, where his models have been
deployed; and market design problems in industry (e.g., online advertising)
through various startups. He received his PhD in computer science from
Carnegie Mellon University (SCS CSD PhD '16).

*In person: *NSH 3305
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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