Projection Robust Wasserstein

Projection Robust Wasserstein methods aim to efficiently compare probability distributions, particularly in high-dimensional spaces, by leveraging projections to reduce computational complexity while maintaining robustness. Current research focuses on developing computationally efficient projection techniques, such as sliced Wasserstein approaches with optimized or random sampling strategies, and incorporating these into autoencoders and other generative models. These advancements improve the accuracy and scalability of optimal transport methods for applications ranging from anomaly detection in hyperspectral imaging to causal inference and generative modeling, offering a powerful tool for analyzing complex data.