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Found 2 papers, 1 papers with code
Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift
We propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as covariate shift.
Toward Extremely Precise Radial Velocities: II. A Tool For Using Multivariate Gaussian Processes to Model Stellar Activity
Rajpaul et al. (2015) and Jones et al. (2017) proposed applying a physically motivated, multivariate Gaussian process (GP) to jointly model the apparent Doppler shift and multiple indicators of stellar activity as a function of time, so as to separate the planetary signal from various forms of stellar variability.
Gaussian Processes
Time Series Analysis
Instrumentation and Methods for Astrophysics
Earth and Planetary Astrophysics
Solar and Stellar Astrophysics
