[49] Mohammad Reza Taesiri, Giang Nguyen, and Anh Nguyen. Visual correspondence-based explanations improve AI robustness and human-AI team accuracy. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=UavQ9HYye6n.
[50] Philipp Tschandl, Cliff Rosendahl, and Harald Kittler. The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, 5(1): 1-9, 2018.
[51] Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance, 2014.
[52] V.N. Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5):988-999, 1999. doi: 10.1109/72.788640.
[53] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2017. URL https://arxiv.org/abs/1706.03762.
[54] Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit. On calibration and out-of-domain generalization, 2021. URL https://arxiv.org/abs/2102.10395.
[55] Alan Q. Wang and Mert R. Sabuncu. A flexible nadaraya-watson head can offer explainable and calibrated classification. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=iEq6lhG403.
[56] Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip S. Yu. Generalizing to unseen domains: A survey on domain generalization, 2022.
[57] Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, and Lei Zhang. Sharpness-aware gradient matching for domain generalization, 2023.
[58] Yixin Wang and Michael I. Jordan. Desiderata for representation learning: A causal perspective, 2022.
[59] Yufei Wang, Haoliang Li, and Alex C. Kot. Heterogeneous domain generalization via domain mixup. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3622-3626, 2020. doi: 10.1109/ICASSP40776.2020.9053273.
[60] Zihao Wang and Victor Veitch. The causal structure of domain invariant supervised representation learning, 2022. URL https://arxiv.org/abs/2208.06987.
[61] G. S. Watson. Smooth regression analysis. Sankhya: The Indian Journal of Statistics, Series A (26):359-372, 1964.
[62] Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing. Deep kernel learning, 2015. URL https://arxiv.org/abs/1511.02222.
[63] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Adversarial domain adaptation with domain mixup, 2019.
[64] Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn. Improving out-of-distribution robustness via selective augmentation, 2022.
[65] Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong. Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data, 2022.
[66] Dequan Zhang, Ning Zhang, Nan Ye, Jianguang Fang, and Xu Han. Hybrid learning algorithm of radial basis function networks for reliability analysis. IEEE Transactions on Reliability, 70 (3):887-900, 2021. doi: 10.1109/TR.2020.3001232.
[67] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization, 2018.