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We present Natural Gradient Boosting (NGBoost), an algorithm for generic probabilistic prediction via gradient boosting.
Bayesian Learning for Neural Networks
Neal, R. M. (1996) · 1996
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Natural Gradient Works Efficiently in Learning
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Greedy Function Approximation: A Gradient Boosting Machine
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Rasmussen, C. E. and Williams, C. K. I. (2005) · 2005
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The geometry of proper scoring rules
Dawid, A. P. (2007) · 2007
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Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
Earlier work this paper cites.
Generalized additive models for location scale and shape (gamlss) in r
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Earlier work this paper cites.
Variational Learning of Inducing Variables in Sparse Gaussian Processes
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Earlier work this paper cites.
BART: Bayesian additive regression trees
Chipman, H. A., George, E. I., and McCulloch, R. E. (2010) · 2010
Earlier work this paper cites.
Practical Variational Inference for Neural Networks
Graves, A. (2011) · 2011
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
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Contrasting probabilistic scoring rules
Machete, R. L. (2013) · 2013
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Dauphin, Y. N., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., and Bengio, Y. (2014) · 2014
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Theory and Applications of Proper Scoring Rules
Dawid, A. P. and Musio, M. (2014) · 2014
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Probabilistic Forecasting
Gneiting, T. and Katzfuss, M. (2014) · 2014
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New insights and perspectives on the natural gradient method
Martens, J. (2014) · 2014
Machine learning meets economics
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Concrete Dropout
Gal, Y., Hron, J., and Kendall, A. (2017) · 2017
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LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017) · 2017
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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Improving palliative care with deep learning
Avati, A., Jung, K., Harman, S., Downing, L., Ng, A., and Shah, N. H. (2018) · 2018
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Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood
Gebetsberger, M., Messner, J. W., Mayr, G. J., and Zeileis, A. (2018) · 2018
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Weight Uncertainty in Neural Network
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
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XGBoost: A Scalable Tree Boosting System
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Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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Accelerating Natural Gradient with Higher-Order Invariance
Song, Y., Song, J., and Ermon, S. (2018) · 2018
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Countdown Regression: Sharp and Calibrated Survival Predictions
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Distributional regression forests for probabilistic precipitation forecasting in complex terrain
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