[40] Prasanta Chandra Mahalanobis. On the generalised distance in statistics. In Proceedings of the national Institute of Science of India, volume 12, pages 49-55, 1936.
[41] Mantas Mazeika, Eric Tang, Andy Zou, Steven Basart, Jun Shern Chan, Dawn Song, David Forsyth, Jacob Steinhardt, and Dan Hendrycks. How would the viewer feel? estimating wellbeing from video scenarios. Advances in Neural Information Processing Systems, 35:18571-18585, 2022.
[42] Chuizheng Meng, Loc Trinh, Nan Xu, James Enouen, and Yan Liu. Interpretability and fairness evaluation of deep learning models on mimic-iv dataset. Scientific Reports, 12(1):7166, 2022.
[43] Norman Mu and Justin Gilmer. Mnist-c: A robustness benchmark for computer vision. arXiv preprint arXiv:1906.02337, 2019.
[44] Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. A self-supervised approach for adversarial robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 262-271, 2020.
[45] Richard Ngo. The alignment problem from a deep learning perspective. arXiv preprint arXiv:2209.00626, 2022.
[46] Anh Nguyen, Jason Yosinski, and Jeff Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 427-436, 2015.
[47] Mikhail S Nikulin et al. Hellinger distance. Encyclopedia of mathematics, 78, 2001.
[48] Alexander Pan, Kush Bhatia, and Jacob Steinhardt. The effects of reward misspecification: Mapping and mitigating misaligned models. arXiv preprint arXiv:2201.03544, 2022.
[49] Jeonghoon Park, Jimin Hong, Radhika Dua, Daehoon Gwak, Yixuan Li, Jaegul Choo, and Edward Choi. Natural attribute-based shift detection. arXiv preprint arXiv:2110.09276, 2021.
[50] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE International Conference on Computer Vision, pages 1406-1415, 2019.
[51] Joshua C Peterson, Joshua T Abbott, and Thomas L Griffiths. Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive science, 42(8):2648-2669, 2018.
[52] Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky. Human uncertainty makes classification more robust. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9617-9626, 2019.
[53] Nikolay Ponomarenko, Vladimir Lukin, Alexander Zelensky, Karen Egiazarian, Marco Carli, and Federica Battisti. Tid2008-a database for evaluation of full-reference visual quality assessment metrics. Advances of modern radioelectronics, 10(4):30-45, 2009.
[54] Inioluwa Deborah Raji, I Elizabeth Kumar, Aaron Horowitz, and Andrew Selfbst. The fallacy of ai functionality. In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 959–972, 2022.
[55] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821-8831. PMLR, 2021.
[56] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
[57] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "why should i trust you?": Explaining the predictions of any classifier, 2016.
[58] Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor. Imagenet-21k pretraining for the masses. arXiv preprint arXiv:2104.10972, 2021.
[59] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022.