[OxCSML Seminar: David Burt, 2pm Thursday 22nd, Small Lecture Theatre]
Tom Rainforth
Speaker: David Burt
Time: 2pm, Thursday 22nd Feb
Location: Small Lecture Theatre, Statistics
Title: Consistent Validation for Predictive Methods in Spatial Settings
Abstract: Spatial prediction tasks are key to weather forecasting, studying air pollution, and other scientific endeavors. Determining how much to trust predictions made by statistical or physical methods is essential for the credibility of scientific conclusions. Unfortunately, classical approaches for validation fail to handle mismatch between locations available for validation and (test) locations where we want to make predictions. This mismatch is often not an instance of covariate shift (as commonly formalized) because the validation and test locations are fixed (e.g., on a grid or at select points) rather than i.i.d. from two distributions. In the present work, we formalize a check on validation methods: that they become arbitrarily accurate as validation data becomes arbitrarily dense. We show that classical and covariate-shift methods can fail this check. We instead propose a method that builds from existing ideas in the covariate-shift literature, but adapts them to the validation data at hand. We prove that our proposal passes our check. And we demonstrate its advantages empirically on simulated and real data.
About: I am a postdoc in Professor Tamara Broderick’s group at the MIT Laboratory For Information and Decision Systems. Previously, I was a PhD student in the Machine Learning Group at the University of Cambridge, supervised by Professor Carl Edward Rasmussen. I am interested in spatial statistics, validation methods for non-i.i.d. data, Gaussian processes and approximate Bayesian inference.
Tom Rainforth
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