{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T16:47:32Z","timestamp":1755794852118,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737180","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T21:05:41Z","timestamp":1754255141000},"page":"3507-3517","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["When Interpretability Meets Generalization: Delta-GAM for Robust Extrapolation in Out-of-Distribution Settings"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9558-7163","authenticated-orcid":false,"given":"Linxiao","family":"Yang","sequence":"first","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2292-7141","authenticated-orcid":false,"given":"Wenwei","family":"Wang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2104-2587","authenticated-orcid":false,"given":"Qiming","family":"Chen","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6269-0863","authenticated-orcid":false,"given":"Zhipeng","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Software &amp; Microelectronics, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5835-7259","authenticated-orcid":false,"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Neural additive models: Interpretable machine learning with neural nets. Advances in neural information processing systems","author":"Agarwal Rishabh","year":"2021","unstructured":"Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, and Geoffrey E Hinton. 2021. Neural additive models: Interpretable machine learning with neural nets. Advances in neural information processing systems, Vol. 34 (2021), 4699-4711."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207720110067421"},{"key":"e_1_3_2_1_3_1","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893(2019)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7632892"},{"key":"e_1_3_2_1_5_1","volume-title":"Random forests. Machine learning","author":"Breiman Leo","year":"2001","unstructured":"Leo Breiman. 2001. Random forests. Machine learning, Vol. 45 (2001), 5-32."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00041-008-9045-x"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143880"},{"key":"e_1_3_2_1_9_1","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, Mario March, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. Journal of machine learning research, Vol. 17, 59 (2016), 1-35.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.293"},{"key":"e_1_3_2_1_11_1","volume-title":"Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ digital medicine","author":"Giuffr\u00e8 Mauro","year":"2023","unstructured":"Mauro Giuffr\u00e8 and Dennis L Shung. 2023. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ digital medicine, Vol. 6, 1 (2023), 186."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-45933-7_12"},{"volume-title":"Statistical models in S","author":"Hastie Trevor J","key":"e_1_3_2_1_13_1","unstructured":"Trevor J Hastie. 2017. Generalized additive models. In Statistical models in S. Routledge, 249-307."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08328-6"},{"key":"e_1_3_2_1_15_1","volume-title":"John HC Gash, and David A Jones","author":"Huntingford Chris","year":"2003","unstructured":"Chris Huntingford, RG Jones, Christel Prudhomme, R Lamb, John HC Gash, and David A Jones. 2003. Regional climate-model predictions of extreme rainfall for a changing climate. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, Vol. 129, 590 (2003), 1607-1621."},{"key":"e_1_3_2_1_16_1","volume-title":"Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine","author":"Kononenko Igor","year":"2001","unstructured":"Igor Kononenko. 2001. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, Vol. 23, 1 (2001), 89-109."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2023.06.032"},{"key":"e_1_3_2_1_18_1","volume-title":"International conference on machine learning. PMLR, 5815-5826","author":"Krueger David","year":"2021","unstructured":"David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. 2021. Out-of-distribution generalization via risk extrapolation (rex). In International conference on machine learning. PMLR, 5815-5826."},{"key":"e_1_3_2_1_19_1","first-page":"11828","article-title":"Learning invariant graph representations for out-of-distribution generalization","volume":"35","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2022. Learning invariant graph representations for out-of-distribution generalization. Advances in Neural Information Processing Systems, Vol. 35 (2022), 11828-11841.","journal-title":"Advances in Neural Information Processing Systems"},{"volume-title":"Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning. In Forty-first International Conference on Machine Learning.","author":"Liu Haoxin","key":"e_1_3_2_1_20_1","unstructured":"Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, and B Aditya Prakash. [n.d.]. Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_21_1","unstructured":"Jiashuo Liu Zheyan Shen Yue He Xingxuan Zhang Renzhe Xu Han Yu and Peng Cui. 2021. Towards out-of-distribution generalization: A survey. arXiv preprint arXiv:2108.13624(2021)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1561\/116.00000192"},{"key":"e_1_3_2_1_23_1","unstructured":"Scott Lundberg. 2017. A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874(2017)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2022.100258"},{"key":"e_1_3_2_1_25_1","volume-title":"International conference on machine learning. PMLR, 8227-8237","author":"Nori Harsha","year":"2021","unstructured":"Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, and Janardhan Kulkarni. 2021. Accuracy, interpretability, and differential privacy via explainable boosting. In International conference on machine learning. PMLR, 8227-8237."},{"key":"e_1_3_2_1_26_1","unstructured":"Harsha Nori Samuel Jenkins Paul Koch and Rich Caruana. 2019. InterpretML: A Unified Framework for Machine Learning Interpretability. ArXiv. https:\/\/www.microsoft.com\/en-us\/research\/publication\/interpretml-a-unified-framework-for-machine-learning-interpretability\/"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2015.1073155"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312228"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2009.00718.x"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"e_1_3_2_1_31_1","unstructured":"S Ruder. 2017. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv preprint arXiv:1706.05098(2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"Tatsunori B Hashimoto, and Percy Liang.","author":"Sagawa Shiori","year":"2019","unstructured":"Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. 2019. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731(2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"pygam: Generalized additive models in python. Zenodo. doi","author":"Serv\u00e9n Daniel","year":"2018","unstructured":"Daniel Serv\u00e9n and Charlie Brummitt. 2018. pygam: Generalized additive models in python. Zenodo. doi, Vol. 10 (2018)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2013.2285579"},{"key":"e_1_3_2_1_35_1","unstructured":"Chuanbiao Song Kun He Liwei Wang and John E Hopcroft. 2018. Improving the generalization of adversarial training with domain adaptation. arXiv preprint arXiv:1810.00740(2018)."},{"key":"e_1_3_2_1_36_1","unstructured":"Theodor Stoecker Nico Hambauer Patrick Zschech and Mathias Kraus. 2024. IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight. arXiv preprint arXiv:2403.11363(2024)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42521-021-00046-2"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"e_1_3_2_1_39_1","unstructured":"Qingsong Wen Liang Sun Fan Yang Xiaomin Song Jingkun Gao Xue Wang and Huan Xu. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478(2020)."},{"key":"e_1_3_2_1_40_1","volume-title":"GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological modelling","author":"Wood Simon N","year":"2002","unstructured":"Simon N Wood and Nicole H Augustin. 2002. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological modelling, Vol. 157, 2-3 (2002), 157-177."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Toronto ON Canada","acronym":"KDD '25"},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737180","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T14:31:55Z","timestamp":1755354715000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":41,"alternative-id":["10.1145\/3711896.3737180","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737180","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}