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Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/WAIN52551.2021.00024"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_2_65_1","volume-title":"NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications.","author":"Mougan Carlos","year":"2022","unstructured":"Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, and Steffen Staab. 2022. Explanation Shift: Detecting distribution shifts on tabular data via the explanation space. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications."},{"key":"e_1_3_2_2_66_1","volume-title":"MNIST-C: A Robustness Benchmark for Computer Vision. ArXiv","author":"Mu Norman","year":"2019","unstructured":"Norman Mu and Justin Gilmer. 2019. MNIST-C: A Robustness Benchmark for Computer Vision. ArXiv, Vol. abs\/1906.02337 (2019)."},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539145"},{"key":"e_1_3_2_2_68_1","volume-title":"Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems","author":"Ovadia Yaniv","year":"2019","unstructured":"Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. 2019. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3533378"},{"key":"e_1_3_2_2_70_1","volume-title":"Image-to-image translation: Methods and applications","author":"Pang Yingxue","year":"2021","unstructured":"Yingxue Pang, Jianxin Lin, Tao Qin, and Zhibo Chen. 2021. 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