Neural Conditional Transport Maps
This work advances conditional optimal transport for applications in generative modeling and black-box model explainability, though it appears incremental as it builds on existing neural and OT methods.
The paper tackles the problem of learning conditional optimal transport maps between probability distributions by introducing a neural framework with a hypernetwork for adaptive mappings, achieving superior performance over baselines and demonstrating high performance in global sensitivity analysis applications.
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.