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Quantile Regression (QR) provides a way to approximate a single conditional quantile.
A note on the summation of Chebyshev series
Clenshaw, C. W. (1955) · 1955
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A method for numerical integration on an automatic computer
Clenshaw, C. W. and Curtis, A. R. (1960) · 1960
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Error analysis of an algorithm for summing certain finite series
Elliott, D. (1968) · 1968
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Error analysis for polynomial evaluation
Newbery, A. C. R. (1974) · 1974
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Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems
Archer, N. P. and Wang, S. (1993) · 1993
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Monotonic networks
Sill, J. (1998) · 1998
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Quantile regression
Koenker, R. and Hallock, K. F. (2001) · 2001
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Inequality constrained quantile regression
Koenker, R. and Ng, P. (2005) · 2005
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Quantile regression forests
Meinshausen, N. and Ridgeway, G. (2006) · 2006
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Is Gauss quadrature better than Clenshaw-Curtis?
Trefethen, L. N. (2008) · 2008
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Noncrossing quantile regression curve estimation
Bondell, H. D., Reich, B. J., and Wang, H. (2010) · 2010
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Monotone and partially monotone neural networks
Daniels, H. and Velikova, M. (2010) · 2010
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
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TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015) · 2015
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Keras (2015)
Chollet, F. et al. (2019) · 2015
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2015) · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. (2015) · 2015
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Improving deep neural networks using softplus units
Zheng, H., Yang, Z., Liu, W., Liang, J., and Li, Y. (2015) · 2015
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Deep lattice networks and partial monotonic functions
You, S., Ding, D., Canini, K., Pfeifer, J., and Gupta, M. R. (2017) · 2017
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Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
Cannon, A. J. (2018) · 2018
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Frequentist uncertainty estimates for deep learning
Tagasovska, N. and Lopez-Paz, D. (2018) · 2018
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Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination
Zhang, W., Quan, H., and Srinivasan, D. (2018) · 2018
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Modelling heterogeneous distributions with an uncountable mixture of asymmetric laplacians
Brando, A., Rodriguez-Serrano, J. A., Vitria, J., and Muñoz, A. R. (2019) · 2019
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · 2016
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Monotonic calibrated interpolated look-up tables
Gupta, M., Cotter, A., Pfeifer, J., Voevodski, K., Canini, K., Mangylov, A., Moczydlowski, W., and Van Esbroeck, A. (2016) · 2016
Cited alongside, same era.
UCI machine learning repository
Dua, D. and Graff, C. (2017) · 2017
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Handbook of Quantile Regression
Koenker, R., Chernozhukov, V., He, X., and Peng, L. (2017) · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 2017
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On the decay rate of Chebyshev coefficients
Majidian, H. (2017) · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017
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Tagasovska, N. and Lopez-Paz, D. (2019) · 2019
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Unconstrained monotonic neural networks
Wehenkel, A. and Louppe, G. (2019) · 2019
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Fully parameterized quantile function for distributional reinforcement learning
Yang, D., Zhao, L., Lin, Z., Qin, T., Bian, J., and Liu, T.-Y. (2019) · 2019
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Vector quantile regression and optimal transport, from theory to numerics
Carlier, G., Chernozhukov, V., De Bie, G., and Galichon, A. (2020) · 2020
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Monotonic cardinality estimation of similarity selection: A deep learning approach
Wang, Y., Xiao, C., Qin, J., Cao, X., Sun, Y., Wang, W., and Onizuka, M. (2020) · 2020
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Non-crossing quantile regression for distributional reinforcement learning
Zhou, F., Wang, J., and Feng, X. (2020) · 2020
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Calibrated multiple-output quantile regression with representation learning
Feldman, S., Bates, S., and Romano, Y. (2021) · 2021
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