[32] J. Ren, M. Zhang, C. Yu, and Z. Liu. Balanced mse for imbalanced visual regression. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 7916-7925. IEEE, 2022.
[33] R. Rothe, R. Timofte, and L. V. Gool. Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis., 126(2-4):144-157, 2018.
[34] T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims. Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning, pages 1670–1679. PMLR, 2016.
[35] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. ECCV (5), 7576:746-760, 2012.
[36] J. Snoek, Y. Ovadia, E. Fertig, B. Lakshminarayanan, S. Nowozin, D. Sculley, J. V. Dillon, J. Ren, and Z. Nado. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 13969-13980, 2019.
[37] H. Song, T. Diethe, M. Kull, and P. Flach. Distribution calibration for regression. In International Conference on Machine Learning, pages 5897-5906. PMLR, 2019.
[38] M. Stadler, B. Charpentier, S. Geisler, D. Zügner, and S. Gunnemann. Graph posterior network: Bayesian predictive uncertainty for node classification. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 18033-18048, 2021.
[39] B. Sun, J. Feng, and K. Saenko. Return of frustratingly easy domain adaptation. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 2058-2065. AAAI Press, 2016.
[40] L. Torgo, R. P. Ribeiro, B. Pfahringer, and P. Branco. SMOTE for regression. In L. Correia, L. P. Reis, and J. Cascalho, editors, Progress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Angra do Heroísmo, Azores, Portugal, September 9-12, 2013. Proceedings, volume 8154 of Lecture Notes in Computer Science, pages 378-389. Springer, 2013.
[41] A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. EMNLP 2018, page 353, 2018.
[42] H. Wang, Y. Ma, H. Ding, and Y. Wang. Context uncertainty in contextual bandits with applications to recommender systems. In AAAI, 2022.
[43] H. Wang, C. Mao, H. He, M. Zhao, T. S. Jaakkola, and D. Katabi. Bidirectional inference networks: A class of deep bayesian networks for health profiling. In AAAI, volume 33, pages 766-773, 2019.
[44] H. Wang, S. Xingjian, and D.-Y. Yeung. Natural-parameter networks: A class of probabilistic neural networks. In NIPS, pages 118-126, 2016.
[45] H. Wang and J. Yan. Self-interpretable time series prediction with counterfactual explanations. In ICML, 2023.
[46] H. Wang and D.-Y. Yeung. Towards bayesian deep learning: A framework and some existing methods. TDKE, 28(12):3395-3408, 2016.
[47] H. Wang and D.-Y. Yeung. A survey on bayesian deep learning. CSUR, 53(5):1-37, 2020.
[48] Y. Yang, H. Wang, and D. Katabi. On multi-domain long-tailed recognition, generalization and beyond. In ECCV, 2022.
[49] Y. Yang, K. Zha, Y. Chen, H. Wang, and D. Katabi. Delving into deep imbalanced regression. In International Conference on Machine Learning, pages 11842-11851. PMLR, 2021.