{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T16:49:17Z","timestamp":1755794957004,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372116"],"award-info":[{"award-number":["62372116"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737118","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T21:07:39Z","timestamp":1754255259000},"page":"2410-2419","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Score-based Generative Modeling for Conditional Independence Testing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0084-4903","authenticated-orcid":false,"given":"Yixin","family":"Ren","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9986-6071","authenticated-orcid":false,"given":"Chenghou","family":"Jin","sequence":"additional","affiliation":[{"name":"Fudan university, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5515-5913","authenticated-orcid":false,"given":"Yewei","family":"Xia","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2350-8224","authenticated-orcid":false,"given":"Li","family":"Ke","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-1592","authenticated-orcid":false,"given":"Longtao","family":"Huang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2093-2839","authenticated-orcid":false,"given":"Hui","family":"Xue","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5544-5347","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"SIAT, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2313-7635","authenticated-orcid":false,"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-2768","authenticated-orcid":false,"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"International conference on machine learning. PMLR, ACM, 214-223","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In International conference on machine learning. PMLR, ACM, 214-223."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/21-AOS2073"},{"key":"e_1_3_2_2_3_1","volume-title":"Conditional independence testing using generative adversarial networks. Advances in neural information processing systems","author":"Bellot Alexis","year":"2019","unstructured":"Alexis Bellot and Mihaela van der Schaar. 2019. Conditional independence testing using generative adversarial networks. Advances in neural information processing systems, Vol. 32 (2019), 2202-2211."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12340"},{"key":"e_1_3_2_2_5_1","first-page":"1","article-title":"A distribution free conditional independence test with applications to causal discovery","volume":"23","author":"Cai Zhanrui","year":"2022","unstructured":"Zhanrui Cai, Runze Li, and Yaowu Zhang. 2022. A distribution free conditional independence test with applications to causal discovery. Journal of Machine Learning Research, Vol. 23, 85 (2022), 1-41.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12265"},{"key":"e_1_3_2_2_7_1","volume-title":"Fast conditional independence test for vector variables with large sample sizes. arXiv","author":"Chalupka Krzysztof","year":"2018","unstructured":"Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt. 2018. Fast conditional independence test for vector variables with large sample sizes. arXiv 2018. arXiv preprint arXiv:1804.02747(2018)."},{"key":"e_1_3_2_2_8_1","volume-title":"International conference on machine learning. PMLR, ACM, 2606-2615","author":"Chwialkowski Kacper","year":"2016","unstructured":"Kacper Chwialkowski, Heiko Strathmann, and Arthur Gretton. 2016. A kernel test of goodness of fit. In International conference on machine learning. PMLR, ACM, 2606-2615."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/67.3.581"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/aop\/1176994663"},{"key":"e_1_3_2_2_11_1","first-page":"132","article-title":"A Permutation-Based Kernel Conditional Independence Test","author":"Doran Gary","year":"2014","unstructured":"Gary Doran, Krikamol Muandet, Kun Zhang, and Bernhard Sch\u00f6lkopf. 2014. A Permutation-Based Kernel Conditional Independence Test.. In UAI. AUAI, 132-141.","journal-title":"UAI. AUAI"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188410"},{"key":"e_1_3_2_2_13_1","volume-title":"A fast, consistent kernel two-sample test. Advances in neural information processing systems","author":"Gretton Arthur","year":"2009","unstructured":"Arthur Gretton, Kenji Fukumizu, Zaid Harchaoui, and Bharath K Sriperumbudur. 2009. A fast, consistent kernel two-sample test. Advances in neural information processing systems, Vol. 22 (2009), 673-681."},{"key":"e_1_3_2_2_14_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), 585-592."},{"key":"e_1_3_2_2_15_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems, Vol. 33 (2020), 6840-6851."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3586589.3586805"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2431"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1088696"},{"key":"e_1_3_2_2_19_1","unstructured":"Adel Javanmard and Mohammad Mehrabi. 2021. Pearson chi-squared conditional randomization test. arXiv preprint arXiv:2111.00027(2021)."},{"key":"e_1_3_2_2_20_1","volume-title":"Conference on Uncertainty in Artificial Intelligence. PMLR, AUAI, 221-230","author":"Jitkrittum Wittawat","year":"2020","unstructured":"Wittawat Jitkrittum, Heishiro Kanagawa, and Bernhard Sch\u00f6lkopf. 2020. Testing goodness of fit of conditional density models with kernels. In Conference on Uncertainty in Artificial Intelligence. PMLR, AUAI, 221-230."},{"key":"e_1_3_2_2_21_1","first-page":"261","article-title":"A linear-time kernel goodness-of-fit test","volume":"30","author":"Jitkrittum Wittawat","year":"2017","unstructured":"Wittawat Jitkrittum, Wenkai Xu, Zolt\u00e1n Szab\u00f3, Kenji Fukumizu, and Arthur Gretton. 2017. A linear-time kernel goodness-of-fit test. Advances in Neural Information Processing Systems, Vol. 30 (2017), 261-270.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_22_1","volume-title":"Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761(2020).","author":"Kong Zhifeng","year":"2020","unstructured":"Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2020. Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761(2020)."},{"key":"e_1_3_2_2_23_1","first-page":"22870","article-title":"Convergence for score-based generative modeling with polynomial complexity","volume":"35","author":"Lee Holden","year":"2022","unstructured":"Holden Lee, Jianfeng Lu, and Yixin Tan. 2022. Convergence for score-based generative modeling with polynomial complexity. Advances in Neural Information Processing Systems, Vol. 35 (2022), 22870-22882.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1489"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26039"},{"key":"e_1_3_2_2_26_1","volume-title":"K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing. In Thirty-seventh Conference on Neural Information Processing Systems. MIT Press, 23321-23344","author":"Li Shuai","year":"2023","unstructured":"Shuai Li, Yingjie Zhang, Hongtu Zhu, Christina Dan Wang, Hai Shu, Ziqi Chen, Zhuoran Sun, and Yanfeng Yang. 2023b. K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing. In Thirty-seventh Conference on Neural Information Processing Systems. MIT Press, 23321-23344."},{"key":"e_1_3_2_2_27_1","volume-title":"The randomized dependence coefficient. Advances in neural information processing systems","author":"Lopez-Paz David","year":"2013","unstructured":"David Lopez-Paz, Philipp Hennig, and Bernhard Sch\u00f6lkopf. 2013. The randomized dependence coefficient. Advances in neural information processing systems, Vol. 26 (2013), 1-9."},{"key":"e_1_3_2_2_28_1","unstructured":"Nikolas Mittag. 2018. A Nonparametric k-Sample Test of Conditional Independence. Technical Report. Working paper of CERGE-EI Prague Czech Republic."},{"key":"e_1_3_2_2_29_1","volume-title":"A measure-theoretic approach to kernel conditional mean embeddings. Advances in neural information processing systems","author":"Park Junhyung","year":"2020","unstructured":"Junhyung Park and Krikamol Muandet. 2020. A measure-theoretic approach to kernel conditional mean embeddings. Advances in neural information processing systems, Vol. 33 (2020), 21247-21259."},{"key":"e_1_3_2_2_30_1","unstructured":"Roman Pogodin Namrata Deka Yazhe Li Danica J Sutherland Victor Veitch and Arthur Gretton. 2022. Efficient conditionally invariant representation learning. arXiv preprint arXiv:2212.08645(2022)."},{"key":"e_1_3_2_2_31_1","unstructured":"Felipe Maia Polo Yuekai Sun and Moulinath Banerjee. 2023. Conditional independence testing under model misspecification. arXiv preprint arXiv:2307.02520(2023)."},{"key":"e_1_3_2_2_32_1","volume-title":"Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125","author":"Ramesh Aditya","year":"2022","unstructured":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, Vol. 1, 2 (2022), 3."},{"key":"e_1_3_2_2_33_1","volume-title":"Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. MIT Press.","author":"Ren Yixin","year":"2024","unstructured":"Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, and Shuigeng Zhou. 2024a. Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. MIT Press."},{"key":"e_1_3_2_2_34_1","volume-title":"Learning Adaptive Kernels for Statistical Independence Tests. In International Conference on Artificial Intelligence and Statistics. PMLR, JMLR, 2494-2502","author":"Ren Yixin","year":"2024","unstructured":"Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, and Shuigeng Zhou. 2024b. Learning Adaptive Kernels for Statistical Independence Tests. In International Conference on Artificial Intelligence and Statistics. PMLR, JMLR, 2494-2502."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i5.25799"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.2307\/3318418"},{"key":"e_1_3_2_2_37_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, JMLR, 938-947","author":"Runge Jakob","year":"2018","unstructured":"Jakob Runge. 2018. Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. In International Conference on Artificial Intelligence and Statistics. PMLR, JMLR, 938-947."},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Machine Learning. PMLR, ACM","author":"Scetbon Meyer","year":"2022","unstructured":"Meyer Scetbon, Laurent Meunier, and Yaniv Romano. 2022. An asymptotic test for conditional independence using analytic kernel embeddings. In International Conference on Machine Learning. PMLR, ACM, 19328-19346."},{"key":"e_1_3_2_2_39_1","volume-title":"Karthikeyan Shanmugam, Alexandros G Dimakis, and Sanjay Shakkottai.","author":"Sen Rajat","year":"2017","unstructured":"Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G Dimakis, and Sanjay Shakkottai. 2017. Model-powered conditional independence test. Advances in neural information processing systems, Vol. 30 (2017), 2955-2965."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Rajen D Shah and Jonas Peters. 2020. The hardness of conditional independence testing and the generalised covariance measure. (2020).","DOI":"10.1214\/19-AOS1857"},{"key":"e_1_3_2_2_41_1","unstructured":"Tianhong Sheng and Bharath K Sriperumbudur. 2019. On distance and kernel measures of conditional independence. arXiv preprint arXiv:1912.01103(2019)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3546258.3546543"},{"key":"e_1_3_2_2_43_1","unstructured":"Hongjian Shi Mathias Drton and Fang Han. 2021a. On Azadkia-Chatterjee's conditional dependence coefficient. arXiv preprint arXiv:2108.06827(2021)."},{"key":"e_1_3_2_2_44_1","volume-title":"International conference on machine learning. PMLR, ACM, 2256-2265","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning. PMLR, ACM, 2256-2265."},{"key":"e_1_3_2_2_45_1","unstructured":"Yang Song and Prafulla Dhariwal. 2023. Improved techniques for training consistency models. arXiv preprint arXiv:2310.14189(2023)."},{"key":"e_1_3_2_2_46_1","unstructured":"Yang Song Prafulla Dhariwal Mark Chen and Ilya Sutskever. 2023. Consistency models. arXiv preprint arXiv:2303.01469(2023)."},{"key":"e_1_3_2_2_47_1","first-page":"1415","article-title":"Maximum likelihood training of score-based diffusion models","volume":"34","author":"Song Yang","year":"2021","unstructured":"Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon. 2021. Maximum likelihood training of score-based diffusion models. Advances in Neural Information Processing Systems, Vol. 34 (2021), 1415-1428.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_48_1","volume-title":"Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, Vol. 32 (2019), 11918-11930."},{"key":"e_1_3_2_2_49_1","first-page":"574","article-title":"Sliced score matching: A scalable approach to density and score estimation. In Uncertainty in Artificial Intelligence","author":"Song Yang","year":"2020","unstructured":"Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon. 2020a. Sliced score matching: A scalable approach to density and score estimation. In Uncertainty in Artificial Intelligence. PMLR, 574-584.","journal-title":"PMLR"},{"key":"e_1_3_2_2_50_1","unstructured":"Yang Song Jascha Sohl-Dickstein Diederik P Kingma Abhishek Kumar Stefano Ermon and Ben Poole. 2020b. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456(2020)."},{"volume-title":"prediction, and search","author":"Spirtes Peter","key":"e_1_3_2_2_51_1","unstructured":"Peter Spirtes, Clark Glymour, and Richard Scheines. 1993. Causation, prediction, and search. MIT press."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1515\/jci-2018-0017"},{"key":"e_1_3_2_2_53_1","volume-title":"A connection between score matching and denoising autoencoders. Neural computation","author":"Vincent Pascal","year":"2011","unstructured":"Pascal Vincent. 2011. A connection between score matching and denoising autoencoders. Neural computation, Vol. 23, 7 (2011), 1661-1674."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.993081"},{"key":"e_1_3_2_2_55_1","unstructured":"Andrew Warren. 2021. Wasserstein conditional independence testing. arXiv preprint arXiv:2107.14184(2021)."},{"key":"e_1_3_2_2_56_1","volume-title":"International Conference on Machine Learning. PMLR, ACM, 6737-6746","author":"Wenliang Li","year":"2019","unstructured":"Li Wenliang, Danica J Sutherland, Heiko Strathmann, and Arthur Gretton. 2019. Learning deep kernels for exponential family densities. In International Conference on Machine Learning. PMLR, ACM, 6737-6746."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i21.34354"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11555"},{"key":"e_1_3_2_2_59_1","unstructured":"Kun Zhang Jonas Peters Dominik Janzing and Bernhard Sch\u00f6lkopf. 2012. Kernel-based conditional independence test and application in causal discovery. arXiv preprint arXiv:1202.3775(2012)."},{"key":"e_1_3_2_2_60_1","unstructured":"Qinyi Zhang Sarah Filippi Seth Flaxman and Dino Sejdinovic. 2017. Feature-to-feature regression for a two-step conditional independence test. (2017)."}],"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\/3711896.3737118","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T14:40:03Z","timestamp":1755355203000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":60,"alternative-id":["10.1145\/3711896.3737118","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737118","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"}}]}}