[Sds-seminars] [Sds-announce] S&DS Seminar, Allen Liu, 2/17/25, KT 13th Floor, Rm. 1327, 12pm-1pm, "Learning Theoretic Foundations for Modern (Data) Science"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science  

 

Allen Liu, MIT

Date: Monday, February 17, 2025

Time: 12:00PM to 1:00PM

Location: Kline Tower, 13th Floor, Rm. 1327 See map
<http://maps.google.com/?q=219+Prospect+Street%2C+New+Haven%2C+CT%2C+06511%2
C+us>  

219 Prospect Street

New Haven, CT 06511

Webcas:
https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=dca6587d-e389-4
dbe-b000-b266011a6bf1 

 

Learning Theoretic Foundations for Modern (Data) Science

 

Information and Abstract:  In this talk, I will explain how fundamental
problems in computational learning theory are at the heart of modern
problems in machine learning and scientific applications and how algorithmic
insights in mathematically tractable models can inspire new solutions in a
wide variety of domains. I will explore two directions. First, I will
explore algorithmic foundations for model stealing of language models.
Model stealing, where a learner tries to recover an unknown model through
query access, is a critical problem in machine learning. Here, I will aim to
build a theoretical foundation for designing model stealing algorithms.
Second, I will introduce Hamiltonian learning, a central computational task
towards understanding and benchmarking quantum systems.  I will highlight
how the lens of learning theory plays a key role in identifying and
circumventing previous barriers and allows us to give efficient algorithms
in settings that were previously conjectured to be intractable.

 

Speaker bio: Allen Liu is currently a fifth-year graduate student in EECS at
MIT, advised by Ankur Moitra. His research is in learning theory, broadly
defined, encompassing classical learning theory and statistics, as well as
problems in modern machine learning and scientific applications such as
quantum information.  His work has been awarded Best Student Paper at QIP in
2024 and featured in popular science media such as Quanta Magazine's Biggest
Breakthroughs in Computer Science for 2024. 

 

Lunch at 11:30am in room 1307
Talk at 12:00-1:00pm in room 1327A

 

For more details and upcoming events visit our website at
<https://statistics.yale.edu/calendar> https://statistics.yale.edu/calendar.


 

Department of Statistics and Data Science

Yale University
Kline Tower

219 Prospect Street
New Haven, CT 06511

 <https://statistics.yale.edu/> https://statistics.yale.edu/

 

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