An Explainable Gaussian Process Auto-encoder for Tabular Data

Wei Zhang; Brian Barr; John Paisley

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arXiv · 2509.00884

An Explainable Gaussian Process Auto-encoder for Tabular Data

Wei Zhang·Brian Barr·John Paisley

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Abstract

Explainable machine learning has attracted much interest in the community where the stakes are high. Counterfactual explanations methods have become an important tool in explaining a black-box model. The recent advances have leveraged the power of generative models such as an autoencoder. In this paper, we propose a novel method using a Gaussian process to construct the auto-encoder architecture for generating counterfactual samples. The resulting model requires fewer learnable parameters and thus is less prone to overfitting. We also introduce a novel density estimator that allows for searching for in-distribution samples. Furthermore, we introduce an algorithm for selecting the optimal regularization rate on density estimator while searching for counterfactuals. We experiment with our method in several large-scale tabular datasets and compare with other auto-encoder-based methods. The results show that our method is capable of generating diversified and in-distribution counterfactual samples.

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BibTeX RIS

Wei Zhang, Brian Barr, John Paisley. 2025-08-31. An Explainable Gaussian Process Auto-encoder for Tabular Data. https://arxiv.org/abs/2509.00884

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