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Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates.
Language models are few-shot learners
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A stochastic approximation method
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Scoring rules for continuous probability distributions
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Regression quantiles
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Generalized Linear Models
McCullagh, P. and Nelder, J. (1983) · 1983
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The tight constant in the dvoretzky-kiefer-wolfowitz inequality
Massart, P. (1990) · 1990
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Optimal smoothing in single-index models
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The conditional distribution of excess returns: An empirical analysis
Foresi, S. and Peracchi, F. (1995) · 1995
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Quantile curves without crossing
He, X. (1997) · 1997
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Source separation in post-nonlinear mixtures
Taleb, A. and Jutten, C. (1999) · 1999
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Random forests
Breiman, L. (2001) · 2001
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Greedy function approximation: a gradient boosting machine
Friedman, J. H. (2001) · 2001
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Boosting with the l2 loss: regression and classification
Bühlmann, P. and Yu, B. (2003) · 2003
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E-statistics: The energy of statistical samples
Székely, G. J. (2003) · 2003
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On a new multivariate two-sample test
Baringhaus, L. and Franz, C. (2004) · 2004
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Quantile regression
Koenker, R. (2005) · 2005
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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F. (2006) · 2006
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Quantile regression forests
Meinshausen, N. (2006) · 2006
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Boosting Algorithms: Regularization, Prediction and Model Fitting
Bühlmann, P. and Hothorn, T. (2007) · 2007
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The geometry of proper scoring rules
Dawid, A. P. (2007) · 2007
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Bayesian density regression
Dunson, D. B., Pillai, N., and Park, J.-H. (2007) · 2007
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
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Covariate shift adaptation by importance weighted cross validation
Sugiyama, M., Krauledat, M., and Müller, K.-R. (2007) · 2007
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A tutorial on conformal prediction
Shafer, G. and Vovk, V. (2008) · 2008
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Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Schölkopf, B. (2009) · 2009
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On the identifiability of the post-nonlinear causal model
Zhang, K. and Hyvärinen, A. (2009) · 2009
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Quantile and probability curves without crossing
Chernozhukov, V., Fernández-Val, I., and Galichon, A. (2010) · 2010
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A generalized linear model with “gaussian” regressor variables
Brillinger, D. R. (2012) · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
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Equivalence of distance-based and rkhs-based statistics in hypothesis testing
Sejdinovic, D., Sriperumbudur, B., Gretton, A., and Fukumizu, K. (2013) · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Distributionally robust bayesian optimization
Kirschner, J., Bogunovic, I., Jegelka, S., and Krause, A. (2020) · 2020
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Distributionally robust neural networks
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P. (2020) · 2020
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Domain adaptation under structural causal models
Chen, Y. and Bühlmann, P. (2021) · 2021
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A causal framework for distribution generalization
Christiansen, R., Pfister, N., Jakobsen, M. E., Gnecco, N., and Peters, J. (2021) · 2021
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Additive functional cox model
Cui, E., Crainiceanu, C. M., and Leroux, A. (2021) · 2021
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Conditional transformation models
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Maximin effects in inhomogeneous large-scale data
Meinshausen, N. and Bühlmann, P. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Adaptive conformal inference under distribution shift
Gibbs, I. and Candes, E. (2021) · 2021
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Normalizing flows for probabilistic modeling and inference
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Anchor regression: Heterogeneous data meet causality
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Double generative adversarial networks for conditional independence testing
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Strictly proper kernel scores and characteristic kernels on compact spaces
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Deep ensembles work, but are they necessary?
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Conformal prediction under feedback covariate shift for biomolecular design
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Hierarchical text-conditional image generation with clip latents
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Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
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Asymptotic statistical analysis of f f -divergence gan
Shen, X., Chen, K., and Zhang, T. (2022) · 2022
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Conformal prediction beyond exchangeability
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J. (2023) · 2023
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First steps toward understanding the extrapolation of nonlinear models to unseen domains
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Rage Against the Mean – A Review of Distributional Regression Approaches
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The Energy of Data and Distance Correlation
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Generative machine learning methods for multivariate ensemble postprocessing
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Distributionally robust optimization with bias and variance reduction
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What is a good imputation under mar missingness?
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Epistemic neural networks
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Distributional principal autoencoders
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