[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]

Just a reminder that this is happening today.

On Fri, Nov 11, 2022 at 4:59 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> 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
>
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