Generative modeling of density regression through tree flows

• URL: http://arxiv.org/abs/2406.05260v3
• Date: Sat, 05 Oct 2024 04:11:53 GMT
• Title: Generative modeling of density regression through tree flows
• Authors: Zhuoqun Wang, Naoki Awaya, Li Ma,
• Abstract summary: We propose a flow-based generative model tailored for the density regression task on tabular data. We introduce a training algorithm for fitting the tree-based transforms using a divide-and-conquer strategy. Our method consistently achieves comparable or superior performance at a fraction of the training and sampling budget.
• Score: 3.0262553206264893
• License:
• Abstract: A common objective in the analysis of tabular data is estimating the conditional distribution (in contrast to only producing predictions) of a set of "outcome" variables given a set of "covariates", which is sometimes referred to as the "density regression" problem. Beyond estimation on the conditional distribution, the generative ability of drawing synthetic samples from the learned conditional distribution is also desired as it further widens the range of applications. We propose a flow-based generative model tailored for the density regression task on tabular data. Our flow applies a sequence of tree-based piecewise-linear transforms on initial uniform noise to eventually generate samples from complex conditional densities of (univariate or multivariate) outcomes given the covariates and allows efficient analytical evaluation of the fitted conditional density on any point in the sample space. We introduce a training algorithm for fitting the tree-based transforms using a divide-and-conquer strategy that transforms maximum likelihood training of the tree-flow into training a collection of binary classifiers--one at each tree split--under cross-entropy loss. We assess the performance of our method under out-of-sample likelihood evaluation and compare it with a variety of state-of-the-art conditional density learners on a range of simulated and real benchmark tabular datasets. Our method consistently achieves comparable or superior performance at a fraction of the training and sampling budget. Finally, we demonstrate the utility of our method's generative ability through an application to generating synthetic longitudinal microbiome compositional data based on training our flow on a publicly available microbiome study.

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