[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]

Reminder that this is happening today!

On Sun, Mar 26, 2023 at 11:51 AM Asher Trockman <ashert at cs.cmu.edu> wrote:

> 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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