[CMU AI Seminar] Special! May 13 at 1:45pm (Hybrid) -- Eric Xing (CMU) -- From Learning, to Meta-Learning, to "Lego-Learning" -- theory, system, and engineering -- AI Seminar sponsored by Morgan Stanley

[Asher Trockman]

Dear all,

We invite you to a special installment of our CMU AI Seminar Series* this
Friday (5/13)* in *GHC 8102 *and on Zoom from *1:45 - 2:45** PM (U.S.
Eastern time)*, sponsored by Morgan Stanley
<https://www.morganstanley.com/about-us/technology/>.

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

On this Friday (5/13), *Eric Xing *(CMU & MBZUAI) will be giving a talk
titled *"From Learning, to Meta-Learning, to "Lego-Learning" -- theory,
system, and engineering"* to share his work towards building machine
learning pipelines and systems that meet highly-demanding industrial
standards.

*Title*: From Learning, to Meta-Learning, to "Lego-Learning" -- theory,
system, and engineering

*Talk Abstract*: Software systems for complex tasks - such as controlling
manufacturing processes in real-time; or writing radiological case reports
within a clinical workflow — are becoming increasingly sophisticated and
consist of a large number of data, model, algorithm, and system elements
and modules. Traditional benchmark/leaderboard-driven bespoke approaches in
the Machine Learning community are not suited to meet the highly demanding
industrial standards beyond algorithmic performance, such as
cost-effectiveness, safety, scalability, and automatability, typically
expected in production systems. In this talk, I discuss some technical
issues toward addressing these challenges: 1) a theoretical framework for
panoramic learning with all experiences; 2) optimization methods to best
the effort for learning under such a principled framework; 3) compositional
strategies for building production-grade ML programs from standard parts. I
will present our recent work toward developing a standard model for
Learning that unifies different special-purpose machine learning paradigms
and algorithms, then a Bayesian blackbox optimization approach to Meta
Learning in the space of hyperparameters, model architectures, and system
configurations, and finally principles and designs of standardized software
Legos that facilitate cost-effective building, training, and tuning of
practical ML pipelines and systems.

*Speaker Bio*: Eric P. Xing is the President of the Mohamed bin Zayed
University of Artificial Intelligence, a Professor of Computer Science at
Carnegie Mellon University, and the Founder and Chairman of Petuum Inc., a
2018 World Economic Forum Technology Pioneer company that builds
standardized artificial intelligence development platform and operating
system for broad and general industrial AI applications. He completed his
PhD in Computer Science at UC Berkeley. His main research interests are the
development of machine learning and statistical methodology; and
composable, automatic, and scalable computational systems, for solving
problems involving automated learning, reasoning, and decision-making in
artificial, biological, and social systems. Prof. Xing currently serves or
has served the following roles: associate editor of the Journal of the
American Statistical Association (JASA), Annals of Applied Statistics
(AOAS), and IEEE Journal of Pattern Analysis and Machine Intelligence
(PAMI); action editor of the Machine Learning Journal (MLJ) and Journal of
Machine Learning Research (JMLR); he is a board member of the International
Machine Learning Society.

*In Person: *GHC 8102
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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