4 papers
Transfer Learning using 66 Diseases for Disease Forecasting Applications
Lauren J Beesley, Alexander C Murph, Dave Osthus +1
Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models have shown that synthesizin…
Leveraging Synthetic and Genetic Data to Improve Epidemic Forecasting
Dave Osthus, Alexander C. Murph, Emma E. Goldberg +4
Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improv…
Bayesian Statistical Inversion for High-Dimensional Computer Model Output and Spatially Distributed Counts
Steven D. Barnett, Robert B. Gramacy, Lauren J. Beesley +4
Data collected by the Interstellar Boundary Explorer (IBEX) satellite, recording heliospheric energetic neutral atoms (ENAs), exhibit a phenomenon that has caused space scientists…
Monotonic warpings for additive and deep Gaussian processes
Steven D. Barnett, Lauren J. Beesley, Annie S. Booth +2
Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is con…