[18] Milad Zafar Nezhad, Dongxiao Zhu, Najibesadat Sadati, Kai Yang, and Phillip Levi. SUBIC: A supervised bi-clustering approach for precision medicine. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 755-760. IEEE, 2017.
[19] Kayur Patel, James Fogarty, James A Landay, and Beverly Harrison. Investigating statistical machine learning as a tool for software development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 667-676, 2008.
[20] Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do ImageNet classifiers generalize to ImageNet? In International Conference on Machine Learning, pages 5389-5400. PMLR, 2019.
[21] Andrew C Miller, Leon A Gatys, Joseph Futoma, and Emily Fox. Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. In Machine Learning for Healthcare Conference, pages 308-336. PMLR, 2021.
[22] Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci. Deep neural networks and tabular data: A survey. arXiv preprint arXiv:2110.01889, 2021.
[23] Ravid Shwartz-Ziv and Amitai Armon. Tabular data: Deep learning is not all you need. Information Fusion, 81:84-90, 2022.
[24] Kaggle. Kaggle machine learning and data science survey, 2017.
[25] Boris van Breugel, Zhaozhi Qian, and Mihaela van der Schaar. Synthetic data, real errors: how (not) to publish and use synthetic data. In International Conference on Machine Learning. PMLR, 2023.
[26] Peter Flach. Performance evaluation in machine learning: the good, the bad, the ugly, and the way forward. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 9808-9814, 2019.
[27] Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvjiotham, and Ali Taylan Cemgil. A fine-grained analysis on distribution shift. In International Conference on Learning Representations, 2021.
[28] Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. In International Conference on Learning Representations, 2018.
[29] Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. Beyond accuracy: Behavioral testing of nlp models with checklist. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4902-4912, 2020.
[30] Paul Röttger, Bertie Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, Janet Pierrehumbert, et al. Hatecheck: Functional tests for hate speech detection models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), page 41. Association for Computational Linguistics, 2021.
[31] Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, and Hanie Sedghi. Leveraging unlabeled data to predict out-of-distribution performance. In International Conference on Learning Representations, 2022.
[32] Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt. Predicting with confidence on unseen distributions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1134-1144, 2021.
[33] Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, and Christopher Ré. Mandoline: Model evaluation under distribution shift. In International Conference on Machine Learning, pages 1617-1629. PMLR, 2021.
[34] Boris van Breugel and Mihaela van der Schaar. Beyond privacy: Navigating the opportunities and challenges of synthetic data. arXiv preprint arXiv:2304.03722, 2023.