Unmasking Trees for Tabular Data

ICLR 2025 Conference Submission12760 Authors

Keywords: tabular data, imputation, missing, generative modeling, tabular, probabilistic prediction, tabular ML

TL;DR: We propose tree-based autoregression for tabular data with SotA results on imputation; our hierarchical partitioning-based modeling of each feature also offers benefits for probabilistic prediction tasks.

Abstract: Despite much work on advanced deep learning and generative modeling techniques for tabular data generation and imputation, traditional methods have continued to win on imputation benchmarks. We herein present UnmaskingTrees, a simple method for tabular imputation (and generation) employing gradient-boosted decision trees which are used to incrementally unmask individual features. This approach offers state-of-the-art performance on imputation, and on generation given training data with missingness; and it has competitive performance on vanilla generation. To solve the conditional generation subproblem, we propose a tabular probabilistic prediction method, BaltoBot, which fits a balanced tree of boosted tree classifiers. Unlike older methods, it requires no parametric assumption on the conditional distribution, accommodating features with multimodal distributions; unlike newer diffusion methods, it offers fast sampling, closed-form density estimation, and flexible handling of discrete variables. We finally consider our two approaches as meta-algorithms, demonstrating in-context learning-based generative modeling with TabPFN.

Primary Area: generative models

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Submission Number: 12760