Generative Regression with IQ-BART
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912468389855232 |
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| author | O'Hagan, Sean Ročková, Veronika |
| author_facet | O'Hagan, Sean Ročková, Veronika |
| contents | Implicit Quantile BART (IQ-BART) posits a non-parametric Bayesian model on the conditional quantile function, acting as a model over a conditional model for $Y$ given $X$. One of the key ingredients is augmenting the observed data $\{(Y_i,X_i)\}_{i=1}^n$ with uniformly sampled values $τ_i$ for $1\leq i\leq n$ which serve as training data for quantile function estimation. Using the fact that the location parameter $μ$ in a $τ$-tilted asymmetric Laplace distribution corresponds to the $τ^{th}$ quantile, we build a check-loss likelihood targeting $μ$ as the parameter of interest. We equip the check-loss likelihood parametrized by $μ=f(X,τ)$ with a BART prior on $f(\cdot)$, allowing the conditional quantile function to vary both in $X$ and $τ$. The posterior distribution over $μ(τ,X)$ can be then distilled for estimation of the {\em entire quantile function} as well as for assessing uncertainty through the variation of posterior draws. Simulation-based predictive inference is immediately available through inverse transform sampling using the learned quantile function. The sum-of-trees structure over the conditional quantile function enables flexible distribution-free regression with theoretical guarantees. As a byproduct, we investigate posterior mean quantile estimator as an alternative to the routine sample (posterior mode) quantile estimator. We demonstrate the power of IQ-BART on time series forecasting datasets where IQ-BART can capture multimodality in predictive distributions that might be otherwise missed using traditional parametric approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Regression with IQ-BART O'Hagan, Sean Ročková, Veronika Methodology Machine Learning Implicit Quantile BART (IQ-BART) posits a non-parametric Bayesian model on the conditional quantile function, acting as a model over a conditional model for $Y$ given $X$. One of the key ingredients is augmenting the observed data $\{(Y_i,X_i)\}_{i=1}^n$ with uniformly sampled values $τ_i$ for $1\leq i\leq n$ which serve as training data for quantile function estimation. Using the fact that the location parameter $μ$ in a $τ$-tilted asymmetric Laplace distribution corresponds to the $τ^{th}$ quantile, we build a check-loss likelihood targeting $μ$ as the parameter of interest. We equip the check-loss likelihood parametrized by $μ=f(X,τ)$ with a BART prior on $f(\cdot)$, allowing the conditional quantile function to vary both in $X$ and $τ$. The posterior distribution over $μ(τ,X)$ can be then distilled for estimation of the {\em entire quantile function} as well as for assessing uncertainty through the variation of posterior draws. Simulation-based predictive inference is immediately available through inverse transform sampling using the learned quantile function. The sum-of-trees structure over the conditional quantile function enables flexible distribution-free regression with theoretical guarantees. As a byproduct, we investigate posterior mean quantile estimator as an alternative to the routine sample (posterior mode) quantile estimator. We demonstrate the power of IQ-BART on time series forecasting datasets where IQ-BART can capture multimodality in predictive distributions that might be otherwise missed using traditional parametric approaches. |
| title | Generative Regression with IQ-BART |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2507.04168 |