An Explainable Gaussian Process Auto-encoder for Tabular Data

• URL: http://arxiv.org/abs/2509.00884v1
• Date: Sun, 31 Aug 2025 14:55:12 GMT
• Title: An Explainable Gaussian Process Auto-encoder for Tabular Data
• Authors: Wei Zhang, Brian Barr, John Paisley,
• Abstract summary: We propose a novel method using a Gaussian process to construct the auto-encoder architecture for generating counterfactual samples.<n>The resulting model requires fewer learnable parameters and thus is less prone to overfitting.<n>We also introduce a novel density estimator that allows for searching for in-distribution samples.
• Score: 6.360918504726019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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