[CMU AI Seminar] September 13 at 12pm (NSH 3305 & Zoom) -- Zico Kolter (CMU) -- New approaches to detecting and adapting to domain shifts in machine learning -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Tuesday (9/13)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the first talk of this semester's *CMU AI Seminar*,
sponsored by SambaNova Systems <https://sambanova.ai/>. The seminar will be
held in person in NSH 3305 with food provided, and it will also be
available 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/>.

On 9/13, *Zico Kolter *(CMU) will be giving a talk titled *"New approaches
to detecting and adapting to domain shifts in machine learning**" *to share
work on evaluating and adapting machine learning models under distribution
shift.

*Title*: New approaches to detecting and adapting to domain shifts in
machine learning

*Talk Abstract*: Machine learning systems, in virtually every deployed
system, encounter data from a qualitatively different distribution than
what they were trained upon. Effectively dealing with this problem, known
as domain shift, is thus perhaps the key challenge in deploying machine
learning methods in practice. In this talk, I will motivate some of these
challenges in domain shift, and highlight some of our recent work on two
topics. First, I will present our work on determining if we can even just
evaluate the performance of machine learning models under distribution
shift, without access to labelled data. And second, I will present work on
how we can better adapt our classifiers to new data distributions, again
assuming access only to unlabelled data in the new domain.

*Speaker Bio*: Zico Kolter <http://zicokolter.com> is an Associate
Professor in the Computer Science Department at Carnegie Mellon University,
and also serves as chief scientist of AI research for the Bosch Center for
Artificial Intelligence. His work spans the intersection of machine
learning and optimization, with a large focus on developing more robust and
rigorous methods in deep learning. In addition, he has worked in a number
of application areas, highlighted by work on sustainability and smart
energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan
Fellowship, and best paper awards at NeurIPS, ICML (honorable mention),
IJCAI, KDD, and PESGM.

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

Thanks,
Asher Trockman
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