{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:57:29Z","timestamp":1776985049764,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"IITP","award":["No. 2020-0-01361"],"award-info":[{"award-number":["No. 2020-0-01361"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539454","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"762-772","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["SOS"],"prefix":"10.1145","author":[{"given":"Jayoung","family":"Kim","sequence":"first","affiliation":[{"name":"Yonsei University, Seoul, South Korea"}]},{"given":"Chaejeong","family":"Lee","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, South Korea"}]},{"given":"Yehjin","family":"Shin","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, South Korea"}]},{"given":"Sewon","family":"Park","sequence":"additional","affiliation":[{"name":"Samsung SDS, Seoul, South Korea"}]},{"given":"Minjung","family":"Kim","sequence":"additional","affiliation":[{"name":"Samsung SDS, Seoul, South Korea"}]},{"given":"Noseong","family":"Park","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, South Korea"}]},{"given":"Jihoon","family":"Cho","sequence":"additional","affiliation":[{"name":"Samsung SDS, Seoul, South Korea"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Commonwealth of Australia 2010 Bureau of Meteorology. https:\/\/www.kaggle.com\/jsphyg\/weather-dataset-rattle-package."},{"key":"e_1_3_2_1_2_1","unstructured":"HackerEarth Machine Learning Challenge-Adopt a buddy. https:\/\/www.kaggle.com\/akash14\/adopt-a-buddy."},{"key":"e_1_3_2_1_3_1","unstructured":"Jonas Adler and Sebastian Lunz. 2018. Banach Wasserstein GAN. In NeurIPS."},{"key":"e_1_3_2_1_4_1","unstructured":"Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein Generative Adversarial Networks. In ICML."},{"key":"e_1_3_2_1_5_1","volume-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)","author":"Bishop Christopher M.","unstructured":"Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag."},{"key":"e_1_3_2_1_6_1","unstructured":"L. Breiman J. Friedman C.J. Stone and R.A. Olshen. 1984. Classification and Regression Trees. Taylor & Francis. https:\/\/books.google.co.kr\/books?id=JwQx- WOmSyQC"},{"key":"e_1_3_2_1_7_1","article-title":"SMOTE: Synthetic Minority over-Sampling Technique","volume":"16","author":"Chawla Nitesh V.","year":"2002","unstructured":"Nitesh V. Chawla, KevinW. Bowyer, Lawrence O. Hall, andW. Philip Kegelmeyer. 2002. SMOTE: Synthetic Minority over-Sampling Technique. J. Artif. Int. Res. 16, 1 (2002).","journal-title":"J. Artif. Int. Res."},{"key":"e_1_3_2_1_8_1","unstructured":"Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever and Pieter Abbeel. 2016. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In NeurIPS."},{"key":"e_1_3_2_1_9_1","unstructured":"Edward Choi Siddharth Biswal A. Bradley Maline Jon Duke F. Walter Stewart and Jimeng Sun. 2017. Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks. (2017). arXiv:1703.06490"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054142"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1958.tb00292.x"},{"key":"e_1_3_2_1_12_1","unstructured":"Tim Dockhorn Arash Vahdat and Karsten Kreis. 2022. Score-Based Generative Modeling with Critically-Damped Langevin Diffusion. In ICLR."},{"key":"e_1_3_2_1_13_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114582"},{"key":"e_1_3_2_1_15_1","unstructured":"Crist\u00f3bal Esteban L. Stephanie Hyland and Gunnar R\u00e4tsch. 2017. Realvalued (Medical) Time Series Generation with Recurrent Conditional GANs. arXiv:1706.02633"},{"key":"e_1_3_2_1_16_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NeurIPS."},{"key":"e_1_3_2_1_17_1","volume-title":"Jesse Bettencourt, Ilya Sutskever, and David Duvenaud.","author":"Grathwohl Will","year":"2018","unstructured":"Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. 2018. Ffjord: Free-form continuous dynamics for scalable reversible generative models. arXiv preprint arXiv:1810.01367 (2018)."},{"key":"e_1_3_2_1_18_1","unstructured":"Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron Courville. 2017. Improved Training of Wasserstein GANs. In NeurIPS."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Hui Han Wen-Yuan Wang and Bing-Huan Mao. 2005. Borderline-SMOTE: A New over-Sampling Method in Imbalanced Data Sets Learning. In ICIC.","DOI":"10.1007\/11538059_91"},{"key":"e_1_3_2_1_20_1","volume-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IJCNN.","author":"He Haibo","year":"2008","unstructured":"Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IJCNN."},{"key":"e_1_3_2_1_21_1","unstructured":"Jonathan Ho Ajay Jain and Pieter Abbeel. 2020. Denoising Diffusion Probabilistic Models. In NeurIPS."},{"key":"e_1_3_2_1_22_1","volume-title":"R\u00e9mi Tachet des Combes, and Ioannis Mitliagkas","author":"Jolicoeur-Martineau Alexia","year":"2020","unstructured":"Alexia Jolicoeur-Martineau, R\u00e9mi Pich\u00e9-Taillefer, R\u00e9mi Tachet des Combes, and Ioannis Mitliagkas. 2020. Adversarial score matching and improved sampling for image generation. arXiv preprint arXiv:2009.05475 (2020)."},{"key":"e_1_3_2_1_23_1","volume-title":"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. In International Conference on Learning Representations.","author":"Jordon James","unstructured":"James Jordon, Jinsung Yoon, and V. D. Mihaela Schaar. 2019. PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_24_1","unstructured":"Jayoung Kim Jinsung Jeon Jaehoon Lee Jihyeon Hyeong and Noseong Park. 2021. OCT-GAN: Neural ODE-Based Conditional Tabular GANs. In TheWebConf."},{"key":"e_1_3_2_1_25_1","unstructured":"Jaehoon Lee Jihyeon Hyeong Jinsung Jeon Noseong Park and Jihoon Cho. 2021. Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis. In NeurIPS."},{"key":"e_1_3_2_1_26_1","unstructured":"M. Lichman. 2013. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_27_1","volume-title":"BAGAN: Data Augmentation with Balancing GAN. CoRR abs\/1803.09655","author":"Mariani Giovanni","year":"2018","unstructured":"Giovanni Mariani, Florian Scheidegger, Roxana Istrate, Costas Bekas, and A. Cristiano I. Malossi. 2018. BAGAN: Data Augmentation with Balancing GAN. CoRR abs\/1803.09655 (2018)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Sankha Subhra Mullick Shounak Datta and Swagatam Das. 2019. Generative Adversarial Minority Oversampling. In ICCV.","DOI":"10.1109\/ICCV.2019.00178"},{"key":"e_1_3_2_1_29_1","unstructured":"Augustus Odena Christopher Olah and Jonathon Shlens. 2017. Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv:1610.09585"},{"key":"e_1_3_2_1_30_1","unstructured":"KANCHARLA PARIMALA and Sumohana Channappayya. 2019. Quality Aware Generative Adversarial Networks. In NeurIPS."},{"key":"e_1_3_2_1_31_1","volume-title":"Kookjin Lee, Jaegul Choo, David Keetae Park, Tanmoy Chakraborty, Hongkyu Park, and Youngmin Kim.","author":"Park Noseong","year":"2018","unstructured":"Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Jaegul Choo, David Keetae Park, Tanmoy Chakraborty, Hongkyu Park, and Youngmin Kim. 2018. MMGAN: Manifold-Matching Generative Adversarial Networks. In ICPR."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Noseong Park Mahmoud Mohammadi Kshitij Gorde Sushil Jajodia Hongkyu Park and Youngmin Kim. 2018. Data Synthesis based on Generative Adversarial Networks. (2018). arXiv:1806.03384","DOI":"10.14778\/3231751.3231757"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492900002920"},{"key":"e_1_3_2_1_34_1","unstructured":"Kashif Rasul Calvin Seward Ingmar Schuster and Roland Vollgraf. 2021. Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. In ICML."},{"key":"e_1_3_2_1_35_1","volume-title":"Real-time prediction of online shoppers' purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications 31 (10","author":"Sakar C Okan","year":"2019","unstructured":"C Okan Sakar, S Olcay Polat, Mete Katircioglu, and Yomi Kastro. 2019. Real-time prediction of online shoppers' purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications 31 (10 2019), 6893--6908."},{"key":"e_1_3_2_1_36_1","unstructured":"Robert E. Schapire. 1999. A Brief Introduction to Boosting. In IJCAI."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0b013e3182315a6d"},{"key":"e_1_3_2_1_38_1","unstructured":"Yang Song Jascha Sohl-Dickstein Diederik P Kingma Abhishek Kumar Stefano Ermon and Ben Poole. 2021. Score-Based Generative Modeling through Stochastic Differential Equations. In ICLR."},{"key":"e_1_3_2_1_39_1","volume-title":"VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. In NeurIPS.","author":"Srivastava Akash","year":"2017","unstructured":"Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, and Charles Sutton. 2017. VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. In NeurIPS."},{"key":"e_1_3_2_1_40_1","article-title":"Visualizing Data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9, 86 (2008).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00142"},{"key":"e_1_3_2_1_42_1","volume-title":"Globaland- local aware data generation for the class imbalance problem","author":"Fan Wenqi","unstructured":"WentaoWang, SuhangWang,Wenqi Fan, Zitao Liu, and Jiliang Tang. 2020. Globaland- local aware data generation for the class imbalance problem. In ICDM. SIAM, 307--315."},{"key":"e_1_3_2_1_43_1","unstructured":"Zhisheng Xiao Karsten Kreis and Arash Vahdat. 2022. Tackling the Generative Learning Trilemma with Denoising Diffusion GANs. In ICLR."},{"key":"e_1_3_2_1_44_1","unstructured":"Lei Xu Maria Skoularidou Alfredo Cuesta-Infante and Kalyan Veeramachaneni. 2019. Modeling Tabular data using Conditional GAN. In NeurIPS."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539454","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539454","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:03Z","timestamp":1750186983000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539454"}},"subtitle":["Score-based Oversampling for Tabular Data"],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":44,"alternative-id":["10.1145\/3534678.3539454","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539454","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}