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.

翻译：我们提出了一种用于学习概率分布之间条件最优传输（OT）映射的神经框架。该方法引入了一种能够同时处理类别型与连续型条件变量的条件机制。核心构成是一个依据输入条件生成传输层参数的超网络，从而构建出优于简单条件方法的自适应映射。全面的消融实验表明，本方法相较于基线配置具有更优性能。此外，我们将其应用于全局敏感性分析，在计算基于OT的敏感度指标方面展现了高性能。该工作推进了条件最优传输领域的前沿发展，使最优传输原理能够更广泛地应用于生成建模、黑箱模型可解释性等高维复杂领域。