This document provides supplementary content to the main paper. A, we expand the discussion on comparing our egocentric metric with a recent planner-based 3D object detection metric. B, we provide more details of the StarPoly architecture and training. D shows more visualization results. It measures the KL-divergence of the planner's prediction based on either the ground truth or the detection.

Intuitively, we seek more accurate descriptions of object geometry when it's more

Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning.

Supplementary material is at the end of this document.

Despite the importance of graph OD and many algorithms being developed for it in recent years, there is no comprehensive benchmark on graph outlier detection, which we believe has hindered the development and understanding of graph OD algorithms.

LLP, prove generalization error bounds associated to two bag generation models, and establish universal consistency with respect to the balanced error rate (BER).

Lastly, we demonstrate the superiority of stochastic proximal methods compared to subgradient-based approaches via extensive experiments.