[ABE-L] Seminário DEST/UFMG em 12/04/2024

[Marcos Prates]

Caros,

Na próxima sexta-feira (12 de Abril, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do prof. Matthias
Katsfuß da University of Wisconsin–Madison - USA.

Matthias Katzfuss is a Professor in the Department of Statistics at
University of Wisconsin–Madison. His research interests include
computational spatial and spatio-temporal statistics, Gaussian processes,
uncertainty quantification, and data assimilation, with applications to
environmental and satellite remote-sensing data. His research has been
funded by NSF, NASA, NOAA, USDA, Sandia National Laboratory, Jet Propulsion
Laboratory, and Texas A&M Institute of Data Science. Matthias is the
recipient of an NSF Career Award, a Fulbright Scholarship, and an Early
Investigator Award from the American Statistical Association’s Section on
Statistics and the Environment

Title: Probabilistic function estimation via nearest-neighbor directed
acyclic graphs

Abstract: We consider probabilistic inference on continuous functions or
fields, such as time series, geospatial fields, response surfaces of
computer models, or regression functions. Gaussian processes (GPs) are
popular models for such applications, but Gaussian assumptions are too
restrictive in many settings. Sparse autoregressive structures
corresponding to nearest-neighbor directed acyclic graphs (NN-DAGs) can
lead to scalable, accurate, and flexible inference. We provide a number of
examples, including so-called Vecchia approximations of GPs, and
autoregressive GPs for learning high-dimensional spatial distributions from
a small number of training samples (e.g., for climate-model emulation).
When the function of interest is latent, we propose a novel framework for
variational inference targeting its potentially non-Gaussian posterior. We
make NN-DAG assumptions for both the prior and variational families, with
highly expressive conditional distributions in the variational family.
Scalable model fitting can be achieved via doubly stochastic variational
optimization with polylogarithmic time complexity per iteration based on
reduced ancestor sets.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG".

https://www.youtube.com/@seminariosdest-ufmg

Att,
Marcos Prates
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <http://lists.ime.usp.br/pipermail/abe/attachments/20240406/3fea26a4/attachment.htm>