{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:24Z","timestamp":1786120824729,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":72,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"funder":[{"name":"National Research Foundation of Korea","award":["RS-2025-24535423"],"award-info":[{"award-number":["RS-2025-24535423"]}]},{"name":"Ministry of Science and ICT, South Korea","award":["S-2025-25422680"],"award-info":[{"award-number":["S-2025-25422680"]}]},{"name":"Ministry of Science and ICT, South Korea","award":["RS-2020-II201373"],"award-info":[{"award-number":["RS-2020-II201373"]}]},{"name":"Samsung","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3818154","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:47Z","timestamp":1786036547000},"page":"2319-2329","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Structure-guided Distributionally Robust Optimization under Domain Shifts"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8506-969X","authenticated-orcid":false,"given":"Seonggyeom","family":"Kim","sequence":"first","affiliation":[{"name":"Dept. of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6920-0015","authenticated-orcid":false,"given":"Eunjung","family":"Choi","sequence":"additional","affiliation":[{"name":"Dept. of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5410-6391","authenticated-orcid":false,"given":"Dong-Kyu","family":"Chae","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science, Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"3438","article-title":"Invariance principle meets information bottleneck for out-of-distribution generalization","volume":"34","author":"Ahuja Kartik","year":"2021","unstructured":"Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish. 2021. Invariance principle meets information bottleneck for out-of-distribution generalization. Advances in Neural Information Processing Systems, Vol. 34 (2021), 3438-3450.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_2_1","volume-title":"Invariant risk minimization. arXiv preprint arXiv:1907.02893","author":"Arjovsky Martin","year":"2019","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_3_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0601"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1120.1641"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2021.1178"},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Dzpe9C1mpiv","author":"Bui Anh Tuan","year":"2022","unstructured":"Anh Tuan Bui, Trung Le, Quan Hung Tran, He Zhao, and Dinh Phung. 2022. A Unified Wasserstein Distributional Robustness Framework for Adversarial Training. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Dzpe9C1mpiv"},{"key":"e_1_3_2_1_8_1","first-page":"22405","article-title":"Swad: Domain generalization by seeking flat minima","volume":"34","author":"Cha Junbum","year":"2021","unstructured":"Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. 2021. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22405-22418.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.3150\/24-BEJ1773"},{"key":"e_1_3_2_1_10_1","first-page":"299","article-title":"On information-type measure of difference of probability distributions and indirect observations","volume":"2","author":"Csisz\u00e1r Imre","year":"1967","unstructured":"Imre Csisz\u00e1r. 1967. On information-type measure of difference of probability distributions and indirect observations. Studia Sci. Math. Hungar., Vol. 2 (1967), 299-318.","journal-title":"Studia Sci. Math. Hungar."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"6817","author":"Dai Rui","year":"2023","unstructured":"Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, and Xinmei Tian. 2023. Moderately Distributional Exploration for Domain Generalization. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 6786-6817. https:\/\/proceedings.mlr.press\/v202\/dai23d.html"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2572"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1344"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33808"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.208"},{"key":"e_1_3_2_1_17_1","volume-title":"Incorporating unlabeled data into distributionally robust learning. arXiv preprint arXiv:1912.07729","author":"Frogner Charlie","year":"2019","unstructured":"Charlie Frogner, Sebastian Claici, Edward Chien, and Justin Solomon. 2019. Incorporating unlabeled data into distributionally robust learning. arXiv preprint arXiv:1912.07729 (2019)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2022.1275"},{"key":"e_1_3_2_1_19_1","volume-title":"A kernel statistical test of independence. Advances in neural information processing systems","author":"Gretton Arthur","year":"2007","unstructured":"Arthur Gretton, Kenji Fukumizu, Choon Teo, Le Song, Bernhard Sch\u00f6lkopf, and Alex Smola. 2007. A kernel statistical test of independence. Advances in neural information processing systems, Vol. 20 (2007)."},{"key":"e_1_3_2_1_20_1","unstructured":"Ishaan Gulrajani and David Lopez-Paz. 2020. In search of lost domain generalization. arXiv preprint arXiv:2007.01434 (2020)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467439"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01756"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Jinyong Jeong Hyungu Kahng and Seoung Bum Kim. 2025. Multi-Expert Distributionally Robust Optimization for Out-of-Distribution Generalization. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=Lz5BUjArK4","DOI":"10.52202\/085713-3384"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679539"},{"key":"e_1_3_2_1_26_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_27_1","first-page":"130","article-title":"Wasserstein distributionally robust optimization: Theory and applications in machine learning. In Operations research & management science in the age of analytics","author":"Kuhn Daniel","year":"2019","unstructured":"Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, and Soroosh Shafieezadeh-Abadeh. 2019. Wasserstein distributionally robust optimization: Theory and applications in machine learning. In Operations research & management science in the age of analytics. Informs, 130-166.","journal-title":"Informs"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.01430"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00128"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.3934\/naco.2021057"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17050"},{"key":"e_1_3_2_1_34_1","unstructured":"Shuang Liu Yihan Wang Yifan Zhu Yibo Miao and Xiao-Shan Gao. 2025. Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=sq5LLWk5SN"},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Learning Representations.","author":"Lu Chaochao","year":"2021","unstructured":"Chaochao Lu, Yuhuai Wu, Jos\u00e9 Miguel Hern\u00e1ndez-Lobato, and Bernhard Sch\u00f6lkopf. 2021. Invariant causal representation learning for out-of-distribution generalization. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00788"},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzIBfZAb","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"key":"e_1_3_2_1_38_1","volume-title":"International conference on machine learning. PMLR, 7313-7324","author":"Mahajan Divyat","year":"2021","unstructured":"Divyat Mahajan, Shruti Tople, and Amit Sharma. 2021. Domain generalization using causal matching. In International conference on machine learning. PMLR, 7313-7324."},{"key":"e_1_3_2_1_39_1","volume-title":"Disentangled Embedding through Style and Mutual Information for Domain Generalization. Transactions on Machine Learning Research","author":"Mehmood Noaman","year":"2025","unstructured":"Noaman Mehmood and Kenneth Barner. 2025. Disentangled Embedding through Style and Mutual Information for Domain Generalization. Transactions on Machine Learning Research (2025). https:\/\/openreview.net\/forum?id=552tedTByb"},{"key":"e_1_3_2_1_40_1","volume-title":"NeurIPS ML Safety Workshop.","author":"Mehra Akshay","year":"2022","unstructured":"Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm. 2022. Do domain generalization methods generalize well?. In NeurIPS ML Safety Workshop."},{"key":"e_1_3_2_1_41_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9p2ekP904Rs","author":"Mitrovic Jovana","year":"2021","unstructured":"Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, and Charles Blundell. 2021. Representation Learning via Invariant Causal Mechanisms. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9p2ekP904Rs"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-017-1172-1"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"e_1_3_2_1_44_1","volume-title":"Stochastic gradient methods for distributionally robust optimization with f-divergences. Advances in neural information processing systems","author":"Namkoong Hongseok","year":"2016","unstructured":"Hongseok Namkoong and John C Duchi. 2016. Stochastic gradient methods for distributionally robust optimization with f-divergences. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_1_45_1","first-page":"17943","article-title":"On the role of sparsity and dag constraints for learning linear dags","volume":"33","author":"Ng Ignavier","year":"2020","unstructured":"Ignavier Ng, AmirEmad Ghassami, and Kun Zhang. 2020. On the role of sparsity and dag constraints for learning linear dags. Advances in Neural Information Processing Systems, Vol. 33 (2020), 17943-17954.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_46_1","volume-title":"Dynamic Spuriosity Bias Harmonizer: Combatting Spurious Features for Domain Generalization. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 44-56","author":"Park Byeongtae","year":"2025","unstructured":"Byeongtae Park, Harim Lee, Sungmoon Kim, and Dong-Kyu Chae. 2025. Dynamic Spuriosity Bias Harmonizer: Combatting Spurious Features for Domain Generalization. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 44-56."},{"key":"e_1_3_2_1_47_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 7644-7664","author":"Phan Hoang","year":"2023","unstructured":"Hoang Phan, Trung Le, Trung Phung, Anh Tuan Bui, Nhat Ho, and Dinh Phung. 2023. Global-local regularization via distributional robustness. In International Conference on Artificial Intelligence and Statistics. PMLR, 7644-7664."},{"key":"e_1_3_2_1_50_1","volume-title":"Structure-informed Risk Minimization for Robust Ensemble Learning. In Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=GvV9MJs268","author":"Qiao Fengchun","year":"2025","unstructured":"Fengchun Qiao, Yanlin Chen, and Xi Peng. 2025. Structure-informed Risk Minimization for Robust Ensemble Learning. In Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=GvV9MJs268"},{"key":"e_1_3_2_1_51_1","volume-title":"Topology-aware Robust Optimization for Out-of-Distribution Generalization. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ylMq8MBnAp","author":"Qiao Fengchun","year":"2023","unstructured":"Fengchun Qiao and Xi Peng. 2023. Topology-aware Robust Optimization for Out-of-Distribution Generalization. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ylMq8MBnAp"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5802\/ojmo.15"},{"key":"e_1_3_2_1_53_1","volume-title":"Distributionally Robust Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryxGuJrFvS","author":"Shiori","year":"2020","unstructured":"Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. 2020. Distributionally Robust Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryxGuJrFvS"},{"key":"e_1_3_2_1_54_1","volume-title":"Causality-oriented robustness: exploiting general noise interventions in linear structural causal models. J. Amer. Statist. Assoc., just-accepted","author":"Shen Xinwei","year":"2025","unstructured":"Xinwei Shen, Peter B\u00fchlmann, and Armeen Taeb. 2025. Causality-oriented robustness: exploiting general noise interventions in linear structural causal models. J. Amer. Statist. Assoc., just-accepted (2025), 1-20."},{"key":"e_1_3_2_1_55_1","volume-title":"Certifiable Distributional Robustness with Principled Adversarial Training. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hk6kPgZA-","author":"Sinha Aman","year":"2018","unstructured":"Aman Sinha, Hongseok Namkoong, and John Duchi. 2018a. Certifiable Distributional Robustness with Principled Adversarial Training. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hk6kPgZA-"},{"key":"e_1_3_2_1_56_1","volume-title":"Certifying Some Distributional Robustness with Principled Adversarial Training. In International Conference on Learning Representations.","author":"Sinha Aman","year":"2018","unstructured":"Aman Sinha, Hongseok Namkoong, and John Duchi. 2018b. Certifying Some Distributional Robustness with Principled Adversarial Training. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_57_1","volume-title":"Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=GB9XiKIwfp","author":"Song Zeen","year":"2025","unstructured":"Zeen Song, Siyu Zhao, Xingyu Zhang, Jiangmeng Li, Changwen Zheng, and Wenwen Qiang. 2025. Learning Invariant Causal Mechanism from Vision-Language Models. In Forty-second International Conference on Machine Learning. https:\/\/openreview.net\/forum?id=GB9XiKIwfp"},{"key":"e_1_3_2_1_58_1","volume-title":"prediction, and search","author":"Spirtes Peter","unstructured":"Peter Spirtes, Clark N Glymour, and Richard Scheines. 2000. Causation, prediction, and search. MIT press."},{"key":"e_1_3_2_1_59_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Staib Matthew","year":"2019","unstructured":"Matthew Staib and Stefanie Jegelka. 2019. Distributionally robust optimization and generalization in kernel methods. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_60_1","volume-title":"International Conference on Machine Learning. PMLR, 6056-6065","author":"Suter Raphael","year":"2019","unstructured":"Raphael Suter, Djordje Miladinovic, Bernhard Sch\u00f6lkopf, and Stefan Bauer. 2019. Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness. In International Conference on Machine Learning. PMLR, 6056-6065."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02219"},{"key":"e_1_3_2_1_62_1","volume-title":"Leveraging Diffusion Latent Space for Domain Generalization. In Second Workshop on Visual Concepts. https:\/\/openreview.net\/forum?id=ay9jEQazmG","author":"Thomas Xavier","year":"2025","unstructured":"Xavier Thomas and Deepti Ghadiyaram. 2025. What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization. In Second Workshop on Visual Concepts. https:\/\/openreview.net\/forum?id=ay9jEQazmG"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"e_1_3_2_1_64_1","volume-title":"Generalizing to unseen domains: A survey on domain generalization","author":"Wang Jindong","year":"2022","unstructured":"Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip S Yu. 2022a. Generalizing to unseen domains: A survey on domain generalization. IEEE transactions on knowledge and data engineering, Vol. 35, 8 (2022), 8052-8072."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00367"},{"key":"e_1_3_2_1_66_1","volume-title":"Differentiable invariant causal discovery. arXiv preprint arXiv:2205.15638","author":"Wang Yu","year":"2022","unstructured":"Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, and Tat-Seng Chua. 2022b. Differentiable invariant causal discovery. arXiv preprint arXiv:2205.15638 (2022)."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00696"},{"key":"e_1_3_2_1_68_1","volume-title":"International conference on machine learning. PMLR, 7472-7482","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning. PMLR, 7472-7482."},{"key":"e_1_3_2_1_69_1","volume-title":"Dags with no tears: Continuous optimization for structure learning. Advances in neural information processing systems","author":"Zheng Xun","year":"2018","unstructured":"Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018. Dags with no tears: Continuous optimization for structure learning. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_70_1","volume-title":"International conference on artificial intelligence and statistics. Pmlr, 3414-3425","author":"Zheng Xun","year":"2020","unstructured":"Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing. 2020. Learning sparse nonparametric dags. In International conference on artificial intelligence and statistics. Pmlr, 3414-3425."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2025.3535297"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01913-8"}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:15:53Z","timestamp":1786065353000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3818154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":72,"alternative-id":["10.1145\/3770855.3818154","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3818154","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}