{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:42:02Z","timestamp":1777614122815,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,21]]},"DOI":"10.1145\/3630744.3658614","type":"proceedings-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T09:44:55Z","timestamp":1718271895000},"page":"32-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Advancing Web Science through Foundation Model for Tabular Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-287X","authenticated-orcid":false,"given":"Inwon","family":"Kang","sequence":"first","affiliation":[{"name":"Computer Science, Rensselaer Polytechnic Institute, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","unstructured":"1999. Internet Usage Data. UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C5Q60G.","DOI":"10.24432\/C5Q60G"},{"key":"e_1_3_2_1_2_1","unstructured":"Yi\u00a0Wang Aden. 2012. KDD Cup 2012 Track 2. https:\/\/kaggle.com\/competitions\/kddcup2012-track2"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Jacob Bien and Robert Tibshirani. 2011. Prototype selection for interpretable classification. (2011).","DOI":"10.1214\/11-AOAS495"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 2nd Machine Learning for Healthcare Conference. PMLR, 286\u2013305","author":"Choi Edward","year":"2017","unstructured":"Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter\u00a0F. Stewart, and Jimeng Sun. 2017. Generating Multi-label Discrete Patient Records Using Generative Adversarial Networks. In Proceedings of the 2nd Machine Learning for Healthcare Conference. PMLR, 286\u2013305."},{"key":"e_1_3_2_1_5_1","unstructured":"Justin Cui and Ruochen Wang. 2022. DC-BENCH: Dataset Condensation Benchmark. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_1_7_1","volume-title":"Generative adversarial nets. Advances in neural information processing systems 27","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_8_1","unstructured":"Yu\u00a0V. Gorishniy Ivan Rubachev Valentin Khrulkov and Artem Babenko. 2021. Revisiting Deep Learning Models for Tabular Data. In Neural Information Processing Systems."},{"key":"e_1_3_2_1_9_1","unstructured":"L\u00e9o Grinsztajn Edouard Oyallon and Ga\u00ebl Varoquaux. 2022. Why do tree-based models still outperform deep learning on tabular data?arxiv:2207.08815\u00a0[cs.LG]"},{"key":"e_1_3_2_1_10_1","volume-title":"Mihaela van\u00a0der Schaar, and Andrija Petrovic","author":"Hansen Lasse","year":"2024","unstructured":"Lasse Hansen, Nabeel Seedat, Mihaela van\u00a0der Schaar, and Andrija Petrovic. 2024. Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_11_1","volume-title":"Using bert to extract topic-independent sentiment features for social media bot detection. In 2020 11th IEEE annual ubiquitous computing, electronics & mobile communication conference (UEMCON)","author":"Heidari Maryam","unstructured":"Maryam Heidari and James\u00a0H Jones. 2020. Using bert to extract topic-independent sentiment features for social media bot detection. In 2020 11th IEEE annual ubiquitous computing, electronics & mobile communication conference (UEMCON). IEEE, 0542\u20130547."},{"key":"e_1_3_2_1_12_1","volume-title":"FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia tools and applications 80, 8","author":"Kaliyar Rohit\u00a0Kumar","year":"2021","unstructured":"Rohit\u00a0Kumar Kaliyar, Anurag Goswami, and Pratik Narang. 2021. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia tools and applications 80, 8 (2021), 11765\u201311788."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Inwon Kang Maruf\u00a0Ahmed Mridul Sanders Abraham Yao Ma Thilanka Munasinghe Aparna Gupta and Oshani Seneviratne. 2024. Deciphering Crypto Twitter.","DOI":"10.1145\/3614419.3644026"},{"key":"e_1_3_2_1_14_1","volume-title":"Effective Data Distillation for Tabular Datasets. In AAAI Conference on Artificial Intelligence.","author":"Kang Inwon","year":"2024","unstructured":"Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, and Oshani Seneviratne. 2024. Effective Data Distillation for Tabular Datasets. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_15_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma P","year":"2013","unstructured":"Diederik\u00a0P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510548.3519377"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 17564\u201317579","author":"Kotelnikov Akim","year":"2023","unstructured":"Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. 2023. Tabddpm: Modelling tabular data with diffusion models. In International Conference on Machine Learning. PMLR, 17564\u201317579."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","unstructured":"Sa\u00efd Ladjal Alasdair Newson and Chi-Hieu Pham. 2019. A PCA-like Autoencoder. https:\/\/doi.org\/10.48550\/arXiv.1904.01277 arxiv:1904.01277\u00a0[cs]","DOI":"10.48550\/arXiv.1904.01277"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/PRDC47002.2019.00050"},{"key":"e_1_3_2_1_20_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach. (Sept","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. (Sept. 2019)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72610-2_29"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","unstructured":"Rami Mohammad and Lee McCluskey. 2015. Phishing Websites. UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C51W2X.","DOI":"10.24432\/C51W2X"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0237861"},{"key":"e_1_3_2_1_24_1","volume-title":"BERTweet: A pre-trained language model for English Tweets. arXiv preprint arXiv:2005.10200","author":"Nguyen Dat\u00a0Quoc","year":"2020","unstructured":"Dat\u00a0Quoc Nguyen, Thanh Vu, and Anh\u00a0Tuan Nguyen. 2020. BERTweet: A pre-trained language model for English Tweets. arXiv preprint arXiv:2005.10200 (2020)."},{"key":"e_1_3_2_1_25_1","unstructured":"Timothy Nguyen Zhourong Chen and Jaehoon Lee. 2021. Dataset Meta-Learning from Kernel Ridge-Regression. arxiv:2011.00050\u00a0[cs stat]"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3288336"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-7b98e3ed-013"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01269"},{"key":"e_1_3_2_1_29_1","volume-title":"The graph neural network model","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli, Marco Gori, Ah\u00a0Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61\u201380."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.11.011"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2017.8285168"},{"key":"e_1_3_2_1_32_1","unstructured":"Tongzhou Wang Jun-Yan Zhu Antonio Torralba and Alexei\u00a0A. Efros. 2020. Dataset Distillation. arxiv:1811.10959\u00a0[cs stat]"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","unstructured":"Qitian Wu Chenxiao Yang and Junchi Yan. 2021. Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. https:\/\/doi.org\/10.48550\/arXiv.2110.04514 arxiv:2110.04514\u00a0[cs]","DOI":"10.48550\/arXiv.2110.04514"},{"key":"e_1_3_2_1_34_1","unstructured":"X. 2024. More on Restricted Use Cases \u2013 Twitter Developers. https:\/\/developer.twitter.com\/en\/developer-terms\/more-on-restricted-use-cases."},{"key":"e_1_3_2_1_35_1","volume-title":"Modeling tabular data using conditional gan. Advances in neural information processing systems 32","author":"Xu Lei","year":"2019","unstructured":"Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional gan. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_36_1","volume-title":"Synthesizing tabular data using generative adversarial networks. arXiv preprint arXiv:1811.11264","author":"Xu Lei","year":"2018","unstructured":"Lei Xu and Kalyan Veeramachaneni. 2018. Synthesizing tabular data using generative adversarial networks. arXiv preprint arXiv:1811.11264 (2018)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"},{"key":"e_1_3_2_1_38_1","first-page":"2640","volume-title":"Proceedings of the 38th International Conference on Machine Learning. PMLR, 12674\u201312685","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao and Hakan Bilen. 2021. Dataset Condensation with Differentiable Siamese Augmentation. In Proceedings of the 38th International Conference on Machine Learning. PMLR, 12674\u201312685. https:\/\/proceedings.mlr.press\/v139\/zhao21a.html ISSN: 2640-3498."},{"key":"e_1_3_2_1_39_1","unstructured":"Bo Zhao Konda\u00a0Reddy Mopuri and Hakan Bilen. 2021. Dataset Condensation with Gradient Matching. http:\/\/arxiv.org\/abs\/2006.05929 arXiv:2006.05929 [cs]."},{"key":"e_1_3_2_1_40_1","volume-title":"Hiek Van\u00a0der Scheer, and Lydia\u00a0Y Chen","author":"Zhao Zilong","year":"2023","unstructured":"Zilong Zhao, Aditya Kunar, Robert Birke, Hiek Van\u00a0der Scheer, and Lydia\u00a0Y Chen. 2023. Ctab-gan+: Enhancing tabular data synthesis. Frontiers in big Data 6 (2023)."}],"event":{"name":"Websci '24: 16th ACM Web Science Conference","location":"Stuttgart Germany","acronym":"Websci '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Companion Proceedings of the 16th ACM Web Science Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630744.3658614","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630744.3658614","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T20:07:54Z","timestamp":1756238874000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630744.3658614"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":40,"alternative-id":["10.1145\/3630744.3658614","10.1145\/3630744"],"URL":"https:\/\/doi.org\/10.1145\/3630744.3658614","relation":{},"subject":[],"published":{"date-parts":[[2024,5,21]]},"assertion":[{"value":"2024-06-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}