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Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone.
Monotone regression splines in action
J. O. Ramsay · 1988
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Photo-z performance for precision cosmology
R. Bordoloi, S. J. Lilly, and A. Amara · 2010
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A Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation
Tomas Dahlen, Bahram Mobasher, Sandra M. Faber, Henry C. Ferguson, Guillermo Barro, Steven L. Finkelstein, Kristian Finlator, Adriano Fontana, Ruth Gruetzbauch, Seth Johnson, Janine Pforr, Mara Salvato, Tommy Wiklind, Stijn Wuyts, Viviana Acquaviva, Mark E. Dickinson, Yicheng Guo, Jiasheng Huang, Kuang-Han Huang, Jeffrey A. Newman, Eric F. Bell, Christopher J. Conselice, Audrey Galametz, Eric Gawiser, Mauro Giavalisco, Norman A. Grogin, Nimish Hathi, Dale Kocevski, Anton M. Koekemoer, David C. Koo, Kyoung-Soo Lee, Elizabeth J. McGrath, Casey Papovich, Michael Peth, Russell Ryan, Rachel Somerville, Benjamin Weiner, and Grant Wilson · 2013
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Xsede: Accelerating scientific discovery
John Towns, Timothy Cockerill, Maytal Dahan, Ian Foster, Kelly Gaither, Andrew Grimshaw, Victor Hazlewood, Scott Lathrop, Dave Lifka, Gregory D. Peterson, Ralph Roskies, J. Ray Scott, and Nancy Wilkins-Diehr · 2014
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Accurate photometric redshift probability density estimation - method comparison and application
Markus Michael Rau, Stella Seitz, Fabrice Brimioulle, Eibe Frank, Oliver Friedrich, Daniel Gruen, and Ben Hoyle · 2015
Cited alongside, same era.
XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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On the realistic validation of photometric redshifts
R. Beck, C. A. Lin, E. E. O. Ishida, F. Gieseke, R. S. de Souza, M. V. Costa-Duarte, M. W. Hattab, and A. Krone-Martins · 2017
Cited alongside, same era.
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
Rafael Izbicki and Ann B. Lee · 2017
Cited alongside, same era.
Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
Cited alongside, same era.
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, R. Izbicki, P. E. Freeman, and A. I. Malz · 2019
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The galaxy morphology-density relation at high redshift with candels
Dritan Kodra · 2019
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Testing goodness of fit of conditional density models with kernels
Wittawat Jitkrittum, Heishiro Kanagawa, and Bernhard Schölkopf · 2020
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Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
S. J. Schmidt, A. I. Malz, J. Y. H. Soo, I. A. Almosallam, M. Brescia, S. Cavuoti, J. Cohen-Tanugi, A. J. Connolly, J. DeRose, P. E. Freeman, M. L. Graham, K. G. Iyer, M. J. Jarvis, J. B. Kalmbach, E. Kovacs, A. B. Lee, G. Longo, C. B. Morrison, J. A. Newman, E. Nourbakhsh, E. Nuss, T. Pospisil, H. Tranin, R. H. Wechsler, R. Zhou, R. Izbicki, and LSST Dark Energy Science Collaboration · 2020
Later among the works it cites.
Diagnostics for conditional density models and bayesian inference algorithms
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David Zhao, Niccolò Dalmasso, Rafael Izbicki, and Ann B Lee · 2021
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