[CMU AI Seminar] March 28 at 12pm (GHC 6115 & Zoom) -- Siddharth Prasad (CMU) -- Bicriteria Multidimensional Mechanism Design with Side Information -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Tuesday (3/28)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of this semester's *CMU AI Seminar*,
sponsored by SambaNova Systems <https://sambanova.ai/>. The seminar will be
held in GHC 6115 *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/>.

This Tuesday (3/28), *Siddharth Prasad* (CMU) will be giving a talk titled
*"**Bicriteria Multidimensional Mechanism Design with Side Information**".*

*Title*: Bicriteria Multidimensional Mechanism Design with Side Information

*Talk Abstract*: We develop a versatile new methodology for
multidimensional mechanism design that incorporates side information about
agent types with the bicriteria goal of generating high social welfare and
high revenue simultaneously. Side information can come from a variety of
sources---examples include advice from a domain expert, predictions from a
machine-learning model trained on historical agent data, or even the
mechanism designer's own gut instinct---and in practice such sources are
abundant. In this work we adopt a prior-free perspective that makes no
assumptions on the correctness, accuracy, or source of the side
information.

First, we design a meta-mechanism that integrates input side information
with an improvement of the classical VCG mechanism. The welfare, revenue,
and incentive properties of our meta-mechanism are characterized by a
number of novel constructions we introduce based on the notion of a *weakest
competitor,* which is an agent that has the smallest impact on welfare. We
then show that our meta-mechanism---when carefully
instantiated---simultaneously achieves strong welfare and revenue
guarantees that are parameterized by errors in the side information. When
the side information is highly informative and accurate, our mechanism
achieves welfare and revenue competitive with the total social surplus, and
its performance decays continuously and gradually as the quality of the
side information decreases.

Finally, we apply our meta-mechanism to a setting where each agent's type
is determined by a constant number of parameters. Specifically, agent types
lie on constant-dimensional subspaces (of the potentially high-dimensional
ambient type space) that are known to the mechanism designer. We use our
meta-mechanism to obtain the first known welfare and revenue guarantees in
this setting.

*Speaker Bio:* Siddharth Prasad <https://www.cs.cmu.edu/~sprasad2/> is a
fourth-year PhD student in the Computer Science Department at Carnegie
Mellon University advised by Nina Balcan and Tuomas Sandholm. His research
interests span machine learning, integer programming, mechanism design,
algorithms, and their various interactions.
He was a student researcher at Google Research during Summer 2022, hosted
by Craig Boutilier and Martin Mladenov. He received a B.S. in math and
computer science from Caltech in 2019.

*In person: *GHC 6115
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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