arXiv:1505.03036
공식 코드 · Papers with Code
Removing systematic errors for exoplanet search via latent causes
원문 초록
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.
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Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis
Debiased Ill-Posed Regression
Statistical Machine Learning for Astronomy -- A Textbook
Active Learning for Manifold Gaussian Process Regression
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
GRASP: Grouped Regression with Adaptive Shrinkage Priors
토론
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