[CMU AI Seminar] November 15 at 12pm (NSH 3305 & Zoom) -- Alexander Terenin (University of Cambridge) -- Pathwise Conditioning and Non-Euclidean Gaussian Processes -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this coming Tuesday (11/15)* 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 NSH 3305 *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/>.

On 11/15, *Alexander Terenin* (University of Cambridge) will be giving a
talk titled *"**Pathwise Conditioning and Non-Euclidean Gaussian Processes*
*".*

*Title*: Pathwise Conditioning and Non-Euclidean Gaussian Processes

*Talk Abstract*: In Gaussian processes, conditioning and computation of
posterior distributions is usually done in a distributional fashion by
working with finite-dimensional marginals. However, there is another way to
think about conditioning: using actual random functions rather than their
probability distributions. This perspective is particularly helpful in
decision-theoretic settings such as Bayesian optimization, where it enables
efficient computation of a wider class of acquisition functions than
otherwise possible. In this talk, we describe these recent advances, and
discuss their broader implications to Gaussian processes. We then present a
class of Gaussian process models on graphs and manifolds, which can enable
one to perform Bayesian optimization while taking into account symmetries
and constraints in an intrinsic manner.

*Speaker Bio*: Alexander Terenin is a Postdoctoral Research Associate at
the University of Cambridge. He is interested in statistical machine
learning, particularly in settings where the data is not fixed, but is
gathered interactively by the learning machine. This leads naturally to
Gaussian processes and data-efficient interactive decision-making systems
such as Bayesian optimization, to areas such as multi-armed bandits and
reinforcement learning, and to techniques for incorporating inductive
biases and prior information such as symmetries into machine learning
models.

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

Thanks,
Asher Trockman
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20221111/c0884d71/attachment.html>