In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, and then estimation is performed on the cleaned data.

We provide theoretical insights that support our method's alignment with standard preference alignment objectives.

Flexible and accurate drag-based editing is a challenging task that has recently garnered significant attention.

In the context of modern machine learning, models deployed in real-world scenarios often encounter diverse data shifts like covariate and semantic shifts, leading to challenges in both out-of-distribution (OOD) generalization and detection.

Please go to https://arxiv.org/abs/2409.00119 for the newest version.