Fetching the paper…
Reading the bibliography…
Conformal methods create prediction bands that control average coverage assuming solely i.i.d.
The limits of distribution-free conditional predictive inference
Barber, R. F., Candès, E. J., Ramdas, A., and Tibshirani, R. J. (2019) · 1903
Earlier work this paper cites.
Conformal prediction with localization
Guan, L. (2019) · 1908
Earlier work this paper cites.
Distributional conformal prediction
Chernozhukov, V., Wüthrich, K., and Zhu, Y. (2019) · 1909
Earlier work this paper cites.
A comparison of some conformal quantile regression methods
Sesia, M. and Candès, E. J. (2019) · 1909
Earlier work this paper cites.
Mixture density networks
Bishop, C. M. (1994) · 1994
Earlier work this paper cites.
Comparison of learning algorithms for handwritten digit recognition
LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., et al. (1995) · 1995
Earlier work this paper cites.
Estimating and visualizing conditional densities
Hyndman, R. J., Bashtannyk, D. M., and Grunwald, G. K. (1996) · 1996
Earlier work this paper cites.
Applied linear statistical models
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996) · 1996
Earlier work this paper cites.
Random forests
Breiman, L. (2001) · 2001
Earlier work this paper cites.
Inductive confidence machines for regression
Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A. (2002) · 2002
Earlier work this paper cites.
On conditional density estimation
De Gooijer, J. G. and Zerom, D. (2003) · 2003
Earlier work this paper cites.
Algorithmic learning in a random world
Vovk, V. et al. (2005) · 2005
Earlier work this paper cites.
Probabilistic symmetries and invariance principles
Kallenberg, O. (2006) · 2006
Earlier work this paper cites.
Quantile regression forests
Meinshausen, N. (2006) · 2006
Cited alongside, same era.
k-means++: The advantages of careful seeding
Arthur, D. and Vassilvitskii, S. (2007) · 2007
Cited alongside, same era.
On-line predictive linear regression
Vovk, V., Nouretdinov, I., Gammerman, A., et al. (2009) · 2009
Cited alongside, same era.
What lies beneath: Using p (z) to reduce systematic photometric redshift errors
Wittman, D. (2009) · 2009
Cited alongside, same era.
Photometric redshift probability distributions for galaxies in the sdss dr8
Sheldon, E. S., Cunha, C. E., Mandelbaum, R., Brinkmann, J., and Weaver, B. A. (2012) · 2012
Cited alongside, same era.
Conditional validity of inductive conformal predictors
Vovk, V. (2012) · 2012
Cited alongside, same era.
Converting high-dimensional regression to high-dimensional conditional density estimation
Izbicki, R. and Lee, A. B. (2017) · 2017
Later among the works it cites.
Photo- z z estimation: An example of nonparametric conditional density estimation under selection bias
Izbicki, R., Lee, A. B., and Freeman, P. E. (2017) · 2017
Later among the works it cites.
Gaussian process conditional density estimation
Dutordoir, V., Salimbeni, H., Hensman, J., and Deisenroth, M. (2018) · 2018
Later among the works it cites.
Distribution-free predictive inference for regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018) · 2018
Later among the works it cites.
Conformalized quantile regression
Romano, Y., Patterson, E., and Candès, E. J. (2019) · 2019
Later among the works it cites.
Least ambiguous set-valued classifiers with bounded error levels
Sadinle, M., Lei, J., and Wasserman, L. (2019) · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tpz: photometric redshift pdfs and ancillary information by using prediction trees and random forests
Carrasco Kind, M. and Brunner, R. J. (2013) · 2013
Cited alongside, same era.
Distribution-free prediction bands for non-parametric regression
Lei, J. and Wasserman, L. (2014) · 2014
Cited alongside, same era.
Nonparametric conditional density estimation in a high-dimensional regression setting
Izbicki, R. and Lee, A. B. (2016) · 2016
Cited alongside, same era.
A spectral model for multimodal redshift estimation
Kügler, S. D., Gianniotis, N., and Polsterer, K. L. (2016) · 2016
Cited alongside, same era.
Dealing with uncertain multimodal photometric redshift estimations
Polsterer, K. L. (2016) · 2016
Cited alongside, same era.
On the realistic validation of photometric redshifts
Beck, R., Lin, C.-A., Ishida, E., Gieseke, F., de Souza, R., Costa-Duarte, M., Hattab, M., Krone-Martins, A., and Collaboration, C. (2017) · 2017
Cited alongside, same era.
Later among the works it cites.
Conformal prediction under covariate shift
Tibshirani, R. J., Barber, R. F., Candès, E. J., and Ramdas, A. (2019) · 2019
Later among the works it cites.
Uncertainty sets for image classifiers using conformal prediction
Angelopoulos, A. N., Bates, S., Jordan, M., and Malik, J. (2020) · 2020
Closest in time.
Conditional density estimation tools in python and r with applications to photometric redshifts and likelihood-free cosmological inference
Dalmasso, N., Pospisil, T., Lee, A. B., Izbicki, R., Freeman, P. E., and Malz, A. I. (2020) · 2020
Closest in time.
Distribution-free conditional predictive bands using density estimators
Izbicki, R., Shimizu, G., and Stern, R. B. (2020) · 2020
Closest in time.
Classification with valid and adaptive coverage
Romano, Y., Sesia, M., and Candes, E. (2020) · 2020
Closest in time.
Evaluation of probabilistic photometric redshift estimation approaches for the rubin observatory legacy survey of space and time (lsst)
Schmidt, S., Malz, A., Soo, J., Almosallam, I., Brescia, M., Cavuoti, S., Cohen-Tanugi, J., Connolly, A., DeRose, J., Freeman, P., et al. (2020) · 2020
Closest in time.
Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction
Cauchois, M., Gupta, S., and Duchi, J. C. (2021) · 2021
Closest in time.