Fetching the paper…
Reading the bibliography…
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting.
The Journal of Machine Learning Research
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014) Dropout: a simple way to prevent neural networks from overfitting · 1958
Earlier work this paper cites.
In P. R. Krishnaiah (Ed.), Multivariate analysis ii
Rosenblatt, M. (1969) Conditional probability density and regression estimators · 1969
Earlier work this paper cites.
Journal of the American Statistical Association
Escobar, M. D. and West, M. (1995) Bayesian density estimation and inference using mixtures · 1995
Earlier work this paper cites.
Journal of Computational and Graphical Statistics
Hyndman, R. J., Bashtannyk, D. M. and Grunwald, G. K. (1996) Estimating and visualizing conditional densities · 1996
Earlier work this paper cites.
Diebold, F. X., Gunther, T. A. and Tay, A. (1997) Evaluating density forecasts
1997
Earlier work this paper cites.
Journal of Multivariate Analysis
He, X. and Shao, Q.-M. (2000) On parameters of increasing dimensions · 2000
Earlier work this paper cites.
Journal of Forecasting
Taylor, J. W. (2000) A quantile regression neural network approach to estimating the conditional density of multiperiod returns · 2000
Earlier work this paper cites.
Journal of Forecasting
Timmermann, A. (2000) Density forecasting in economics and finance · 2000
Earlier work this paper cites.
In European Conference on Machine Learning
Frank, E. and Hall, M. (2001) A simple approach to ordinal classification · 2001
Earlier work this paper cites.
Annals of statistics
Friedman, J. H. (2001) Greedy function approximation: a gradient boosting machine · 2001
Earlier work this paper cites.
Journal of economic perspectives
Koenker, R. and Hallock, K. F. (2001) Quantile regression · 2001
Earlier work this paper cites.
Journal of Nonparametric Statistics
Hyndman, R. J. and Yao, Q. (2002) Nonparametric estimation and symmetry tests for conditional density functions · 2002
Cited alongside, same era.
In International Conference on Neural Information Processing
Song, X., Yang, K. and Pavel, M. (2004) Density boosting for gaussian mixtures · 2004
Cited alongside, same era.
Rojas, A. L., Genovese, C. R., Miller, C. J., Nichol, R. and Wasserman, L. (2005) Conditional density estimation using finite mixture models with an application to astrophysics
2005
Cited alongside, same era.
Journal of Machine Learning Research
Meinshausen, N. (2006) Quantile regression forests · 2006
Cited alongside, same era.
Neural Networks
Shrestha, D. L. and Solomatine, D. P. (2006) Machine learning approaches for estimation of prediction interval for the model output · 2006
Cited alongside, same era.
Springer Science & Business Media
Wasserman, L. (2006) All of nonparametric statistics · 2006
IEEE Transactions on Neural Networks
Khosravi, A., Nahavandi, S., Creighton, D. and Atiya, A. F. (2011) Comprehensive review of neural network-based prediction intervals and new advances · 2011
Later among the works it cites.
Journal of Machine Learning Research
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. et al. (2011) Scikit-learn: Machine learning in python · 2011
Later among the works it cites.
arXiv preprint arXiv:1206.5278
Holmes, M. P., Gray, A. G. and Isbell, C. L. (2012) Fast nonparametric conditional density estimation · 2012
Later among the works it cites.
International Journal of Forecasting
Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A. and Hyndman, R. J. (2016) Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond · 2014
Later among the works it cites.
In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y. and Zheng, X. (2016) Tensorflow: A system for large-scale machine learning · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Journal of the Royal Statistical Society: Series A (Statistics in Society)
Fahey, M. T., Thane, C. W., Bramwell, G. D. and Coward, W. A. (2007) Conditional gaussian mixture modelling for dietary pattern analysis · 2007
Cited alongside, same era.
Journal of the American Statistical Association
Gneiting, T. and Raftery, A. E. (2007) Strictly proper scoring rules, prediction, and estimation · 2007
Cited alongside, same era.
Geographical Analysis
Wilson, T. and Bell, M. (2007) Probabilistic regional population forecasts: The example of queensland, australia · 2007
Cited alongside, same era.
In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
Cheng, J., Wang, Z. and Pollastri, G. (2008) A neural network approach to ordinal regression · 2008
Cited alongside, same era.
Acta Mathematicae Applicatae Sinica, English Series
Fan, J.-q., Peng, L., Yao, Q.-w. and Zhang, W.-y. (2009) Approximating conditional density functions using dimension reduction · 2009
Cited alongside, same era.
Later among the works it cites.
Journal of Computational and Graphical Statistics
Izbicki, R. and Lee, A. B. (2016) Nonparametric conditional density estimation in a high-dimensional regression setting · 2016
Later among the works it cites.
Monthly Weather Review
Taillardat, M., Mestre, O., Zamo, M. and Naveau, P. (2016) Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics · 2016
Later among the works it cites.
In NIPS-W
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L. and Lerer, A. (2017) Automatic differentiation in pytorch · 2017
Later among the works it cites.
In 2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Zhu, L. and Laptev, N. (2017) Deep and confident prediction for time series at uber · 2017
Later among the works it cites.
Renewable and Sustainable Energy Reviews
Van der Meer, D. W., Widén, J. and Munkhammar, J. (2018) Review on probabilistic forecasting of photovoltaic power production and electricity consumption · 2018
Later among the works it cites.
arXiv preprint arXiv:1808.08798
Rodrigues, F. and Pereira, F. C. (2018) Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems · 2018
Later among the works it cites.