Distance-informed Neural Processes

• URL: http://arxiv.org/abs/2508.18903v1
• Date: Tue, 26 Aug 2025 10:19:36 GMT
• Title: Distance-informed Neural Processes
• Authors: Aishwarya Venkataramanan, Joachim Denzler,
• Abstract summary: Distance-informed Neural Process (DNP) improves uncertainty estimation by combining global and distance-aware local structures.<n> Empirical results demonstrate that DNP achieves strong predictive performance and improved uncertainty calibration across regression and classification tasks.
• Score: 6.717752897431761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent structures. Standard Neural Processes (NPs) often rely on a global latent variable and struggle with uncertainty calibration and capturing local data dependencies. DNP addresses these limitations by introducing a global latent variable to model task-level variations and a local latent variable to capture input similarity within a distance-preserving latent space. This is achieved through bi-Lipschitz regularization, which bounds distortions in input relationships and encourages the preservation of relative distances in the latent space. This modeling approach allows DNP to produce better-calibrated uncertainty estimates and more effectively distinguish in- from out-of-distribution data. Empirical results demonstrate that DNP achieves strong predictive performance and improved uncertainty calibration across regression and classification tasks.

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