Treeffuser: Probabilistic predictions via conditional diffusions with gradient-boosted trees Conference Paper
| Authors: | Beltran-Velez, N.; Grande, A. A.; Nazaret, A.; Kucukelbir, A.; Blei, D. |
| Title: | Treeffuser: Probabilistic predictions via conditional diffusions with gradient-boosted trees |
| Conference Title: | 2024 Conference on Neural Information Processing Systems (NeurIPS) |
| Abstract: | Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson distributions. When these assumptions fail, such models lead to bad predictions and poorly calibrated uncertainty. In this paper, we propose Treeffuser, an easy-to-use method for probabilistic prediction on tabular data. The idea is to learn a conditional diffusion model where the score function is estimated using gradient-boosted trees. The conditional diffusion model makes Treeffuser flexible and non-parametric, while the gradient-boosted trees make it robust and easy to train on CPUs. Treeffuser learns well-calibrated predictive distributions and can handle a wide range of regression tasks-including those with multivariate, multimodal, and skewed responses. We study Treeffuser on synthetic and real data and show that it outperforms existing methods, providing better calibrated probabilistic predictions. We further demonstrate its versatility with an application to inventory allocation under uncertainty using sales data from Walmart. We implement Treeffuser in https://github.com/blei-lab/treeffuser. © 2024 Neural information processing systems foundation. All rights reserved. |
| Journal Title | Advances in Neural Information Processing Systems |
| Volume: | 37 |
| Conference Dates: | 2024 Dec 10-15 |
| Conference Location: | Vancouver, Canada |
| ISBN: | 1049-5258 |
| Publisher: | Neural Information Processing Systems Foundation |
| Date Published: | 2024-01-01 |
| Language: | English |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference paper -- Source: Scopus |
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