Wednesday, September 20, 2023
Time | Items |
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All day |
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3pm |
09/20/2023 - 3:00pm Regression methods provide a simple mechanism for evaluating empirical functions over scattered data points, allowing to forecast future values. In particular, kernel-based regressions, such as the Nadaraya-Watson estimator are suitable for cases in which the relationship between the data points and the function is not linear. Successive applications of the Nadarya-Watson estimator, also known as Laplacian Pyramids model or iterative bias reduction, yield a multiscale model that generates a smoothed version of a function by using Gaussian kernels to smooth the residuals. In this talk, I will review some recent extensions of this regression for spatio-temporal forecasting and bio-medical applications.
Location:
LOM 206
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4pm |
09/20/2023 - 4:00pm Ebru Toprak: Hydrogenic ions and dispersion Subhadip Dey: Anosov subgroups of semisimple Lie groups Nick Ovenhouse: Laurent Phenomenon for Cluster Algebras and Cluster Superalgebras Gurbir Dhillon: Character formulas, old and new Location:
KT 219
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