Neural Conditional Transport Maps

• URL: http://arxiv.org/abs/2505.15808v1
• Date: Wed, 21 May 2025 17:59:02 GMT
• Title: Neural Conditional Transport Maps
• Authors: Carlos Rodriguez-Pardo, Leonardo Chiani, Emanuele Borgonovo, Massimo Tavoni,
• Abstract summary: We introduce a conditioning mechanism capable of processing both categorical and continuous conditioning variables simultaneously.<n>At the core of our method lies a hypernetwork that generates transport layer parameters based on these inputs, creating adaptive mappings.<n>This work advances the state-of-the-art in conditional optimal transport, enabling broader application of optimal transport principles to complex, high-dimensional domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present a neural framework for learning conditional optimal transport (OT) maps between probability distributions. Our approach introduces a conditioning mechanism capable of processing both categorical and continuous conditioning variables simultaneously. At the core of our method lies a hypernetwork that generates transport layer parameters based on these inputs, creating adaptive mappings that outperform simpler conditioning methods. Comprehensive ablation studies demonstrate the superior performance of our method over baseline configurations. Furthermore, we showcase an application to global sensitivity analysis, offering high performance in computing OT-based sensitivity indices. This work advances the state-of-the-art in conditional optimal transport, enabling broader application of optimal transport principles to complex, high-dimensional domains such as generative modeling and black-box model explainability.

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