Fetching the paper…
Reading the bibliography…
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings.
Box, G.E., Cox, D.R.: An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological) 26
1964
Earlier work this paper cites.
Gelfand, A.E., Dey, D.K.: Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society: Series B (Methodological) 56
1994
Earlier work this paper cites.
Foresi, S., Peracchi, F.: The conditional distribution of excess returns: An empirical analysis. Journal of the American Statistical Association 90
1995
Earlier work this paper cites.
Kooperberg, C., Stone, C.J., Truong, Y.K.: Hazard regression. Journal of the American Statistical Association 90
1995
Earlier work this paper cites.
Hora, S.C.: Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliability Engineering & System Safety 54
1996
Earlier work this paper cites.
Wood, S.N.: Thin plate regression splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65
2003
Earlier work this paper cites.
Koenker, R.: Quantile Regression, vol. Economic Society Monographs. Cambridge University Press (2005)
2005
Earlier work this paper cites.
Rigby, R.A., Stasinopoulos, D.M.: Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics) 54
2005
Earlier work this paper cites.
Meinshausen, N.: Quantile regression forests. Journal of Machine Learning Research 7
2006
Earlier work this paper cites.
Farouki, R.T.: The Bernstein polynomial basis: A centennial retrospective. Computer Aided Geometric Design 29
2012
Earlier work this paper cites.
Chernozhukov, V., Fernández-Val, I., Melly, B.: Inference on counterfactual distributions. Econometrica 81
2013
Earlier work this paper cites.
Fahrmeir, L., Kneib, T., Lang, S., Marx, B.: Regression: Models, Methods and Applications. Springer Berlin Heidelberg (2013)
2013
Earlier work this paper cites.
Rothe, C., Wied, D.: Misspecification testing in a class of conditional distributional models. Journal of the American Statistical Association 108
2013
Earlier work this paper cites.
Tabak, E.G., Turner, C.V.: A family of nonparametric density estimation algorithms. Communications on Pure and Applied Mathematics 66
2013
Earlier work this paper cites.
Wu, C.O., Tian, X.: Nonparametric estimation of conditional distributions and rank-tracking probabilities with time-varying transformation models in longitudinal studies. Journal of the American Statistical Association 108
2013
Earlier work this paper cites.
Hothorn, T., Kneib, T., Bühlmann, P.: Conditional transformation models. Journal of the Royal Statistical Society: Series B: Statistical Methodology pp. 3–27 (2014)
2014
Cited alongside, same era.
Senge, R., Bösner, S., Dembczyński, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., Hüllermeier, E.: Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences 255
2014
Cited alongside, same era.
Leorato, S., Peracchi, F.: Comparing Distribution and Quantile Regression. EIEF Working Papers Series 1511, Einaudi Institute for Economics and Finance (EIEF) (2015)
2015
Cited alongside, same era.
Rezende, D., Mohamed, S.: Variational inference with normalizing flows. Proceedings of Machine Learning Research, vol. 37, pp. 1530–1538. PMLR (2015)
2015
Cited alongside, same era.
Jaini, P., Selby, K.A., Yu, Y.: Sum-of-squares polynomial flow. CoRR (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Müller, T., McWilliams, B., Rousselle, F., Gross, M., Novák, J.: Neural importance sampling (2019)
2019
Later among the works it cites.
Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S., Lakshminarayanan, B.: Normalizing flows for probabilistic modeling and inference (2019)
2019
Later among the works it cites.
Pratola, M., Chipman, H., George, E.I., McCulloch, R.: Heteroscedastic BART via multiplicative regression trees. Journal of Computational and Graphical Statistics (2019)
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)
2015
Cited alongside, same era.
Gupta, M., Cotter, A., Pfeifer, J., Voevodski, K., Canini, K., Mangylov, A., Moczydlowski, W., van Esbroeck, A.: Monotonic calibrated interpolated look-up tables. Journal of Machine Learning Research 17
2016
Cited alongside, same era.
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in neural information processing systems. pp. 5574–5584 (2017)
2017
Cited alongside, same era.
Depeweg, S., Hernandez-Lobato, J.M., Doshi-Velez, F., Udluft, S.: Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning. In: International Conference on Machine Learning. pp. 1184–1193. PMLR (2018)
2018
Cited alongside, same era.
Hothorn, T., Möst, L., Bühlmann, P.: Most likely transformations. Scandinavian Journal of Statistics 45
2018
Cited alongside, same era.
Kuleshov, V., Fenner, N., Ermon, S.: Accurate uncertainties for deep learning using calibrated regression 80
2018
Cited alongside, same era.
Athey, S., Tibshirani, J., Wager, S., et al.: Generalized random forests. The Annals of Statistics 47
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Hothorn, T.: Most likely transformations: The mlt package. Journal of Statistical Software, Articles 92
2020
Closest in time.
Hothorn, T.: Transformation boosting machines. Statistics and Computing 30
2020
Closest in time.
Kobyzev, I., Prince, S., Brubaker, M.: Normalizing flows: An introduction and review of current methods. IEEE Transactions on Pattern Analysis and Machine Intelligence p. 1–1 (2020). https://doi.org/10.1109/tpami.2020.2992934
2020
Closest in time.
2020
Closest in time.
Rothfuss, J., Ferreira, F., Boehm, S., Walther, S., Ulrich, M., Asfour, T., Krause, A.: Noise regularization for conditional density estimation (2020)
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
Hothorn, T., Zeileis, A.: Predictive distribution modeling using transformation forests. Journal of Computational and Graphical Statistics pp. 1–16 (2021)
2021
Closest in time.
Ramasinghe, S., Fernando, K., Khan, S., Barnes, N.: Robust normalizing flows using bernstein-type polynomials (2021)
2021
Closest in time.