Selection Bias

Is augmentation effective in improving prediction in imbalanced datasets?
G. O. Assunção, Rafael Izbicki, M. O. Prates
February, 2024 Machine Learning
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Classification under Prior Probability Shift in Simulator-Based Inference: Application to Atmospheric Cosmic-Ray Showers
A. Shen, L. Masserano, Rafael Izbicki, T. Dorigo, M. Doro, A. B. Lee
March, 2023 NeurIPS (Machine Learning and the Physical Sciences Workshop; Best Poster Award)
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Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
N. Dalmasso, T. Pospisil, A. B. Lee, Rafael Izbicki, P. E. Freeman, A. I. Malz
January, 2020 Astronomy and Computing
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A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting
Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the …
P.E. Freeman, Rafael Izbicki, A.B. Lee
October, 2017 Monthly Notices of the Royal Astronomical Society
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Photo-z estimation: An example of nonparametric conditional density estimation under selection bias
We describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two or more estimators for optimal performance. This leads to new improved photo-z estimators. We illustrate our methods on data from the Sloan Data Sky Survey and an application to galaxy-galaxy lensing.
Rafael Izbicki, Ann B. Lee, Peter E. Freeman
February, 2017 The Annals of Applied Statistics
Preprint PDF
Photo-z estimation: An example of nonparametric conditional density estimation under selection bias