{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T05:34:44Z","timestamp":1768109684337,"version":"3.49.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"BSF - the US-Israel Binational Science foundation","award":["2018194"],"award-info":[{"award-number":["2018194"]}]},{"DOI":"10.13039\/501100003977","name":"iSF - the Israel Science foundation","doi-asserted-by":"crossref","award":["2707\/22"],"award-info":[{"award-number":["2707\/22"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,3,12]]},"abstract":"<jats:p>Tabular embedding methods have become increasingly popular due to their effectiveness in improving the results of various tasks, including classic databases tasks and machine learning predictions. However, most current methods treat these embedding models as \"black boxes\" making it difficult to understand the insights captured by the models. Our research proposes a novel approach to interpret these models, aiming to provide local and global explanations for the original data and detect potential flaws in the embedding models. The proposed solution is appropriate for every tabular embedding algorithm, as it fits the black box view of the embedding model. Furthermore, we propose methods for comparing different embedding models, which can help identify data biases that might impact the models' credibility without the user's knowledge. Our approach is evaluated on multiple datasets and multiple embeddings, demonstrating that our proposed explanations provide valuable insights into the behavior of tabular embedding methods. By making these models more transparent, we believe our research will contribute to the development of more effective and reliable embedding methods for a wide range of applications.<\/jats:p>","DOI":"10.1145\/3639329","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T18:51:32Z","timestamp":1711479092000},"page":"1-26","source":"Crossref","is-referenced-by-count":5,"title":["TabEE: Tabular Embeddings Explanations"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0220-4419","authenticated-orcid":false,"given":"Roni","family":"Copul","sequence":"first","affiliation":[{"name":"Tel Aviv University, Tel Aviv, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8002-0578","authenticated-orcid":false,"given":"Nave","family":"Frost","sequence":"additional","affiliation":[{"name":"eBay Research, Netanya, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8566-8821","authenticated-orcid":false,"given":"Tova","family":"Milo","sequence":"additional","affiliation":[{"name":"Tel Aviv University, Tel Aviv, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9165-2025","authenticated-orcid":false,"given":"Kathy","family":"Razmadze","sequence":"additional","affiliation":[{"name":"Tel Aviv University, Tel Aviv, Israel"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2015. Flights Dataset. https:\/\/www.kaggle.com\/usdot\/flight-delays'select=flights.csv."},{"key":"e_1_2_1_2_1","unstructured":"2020. Spotify Dataset. https:\/\/www.kaggle.com\/datasets\/mrmorj\/dataset-of-songs-in-spotify."},{"key":"e_1_2_1_3_1","unstructured":"2023. TabEE git repository. https:\/\/github.com\/KathyRaz\/TabEE."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00633-6"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2735361"},{"key":"e_1_2_1_7_1","first-page":"1529","article-title":"ExRank: An exploratory ranking interface","volume":"9","author":"Bespinyowong Ramon","year":"2016","unstructured":"Ramon Bespinyowong,Wei Chen, HV Jagadish, and Yuxin Ma. 2016. ExRank: An exploratory ranking interface. PVLBD 9, 13 (2016), 1529--1532.","journal-title":"PVLBD"},{"key":"e_1_2_1_8_1","volume-title":"Local interpretable model-agnostic explanations (LIME). Explanatory Model Analysis","author":"Biecek Przemyslaw","year":"2021","unstructured":"Przemyslaw Biecek and Tomasz Burzykowski. 2021. Local interpretable model-agnostic explanations (LIME). Explanatory Model Analysis; Chapman and Hall\/CRC: New York, NY, USA (2021), 107--123."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","unstructured":"Jock Blackard. 1998. Covertype. UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C50K5N.","DOI":"10.24432\/C50K5N"},{"key":"e_1_2_1_10_1","unstructured":"Rajesh Bordawekar and Oded Shmueli. 2019. Exploiting latent information in relational databases via word embedding and application to degrees of disclosure. In CIDR."},{"key":"e_1_2_1_11_1","volume-title":"29th Italian Symposium on Advanced Database Systems (SEDB)","author":"Cappuzzo Riccardo","year":"2021","unstructured":"Riccardo Cappuzzo, Paolo Papotti, and Saravanan Thirumuruganathan. 2021. Embdi: generating embeddings for relational data integration. In 29th Italian Symposium on Advanced Database Systems (SEDB), Pizzo Calabro, Italy."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2501040.2501981"},{"key":"e_1_2_1_13_1","volume-title":"Data sketching. Commun. ACM 60, 9","author":"Cormode Graham","year":"2017","unstructured":"Graham Cormode. 2017. Data sketching. Commun. ACM 60, 9 (2017)."},{"key":"e_1_2_1_14_1","article-title":"Linear dimensionality reduction: Survey, insights, and generalizations","volume":"16","author":"Cunningham John P","year":"2015","unstructured":"John P Cunningham and Zoubin Ghahramani. 2015. Linear dimensionality reduction: Survey, insights, and generalizations. J. Mach. Learn. Res. 16, 1 (2015).","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183399.3183424"},{"key":"e_1_2_1_16_1","volume-title":"International Conference on Machine Learning. PMLR, 4794--4815","author":"Dasgupta Sanjoy","year":"2022","unstructured":"Sanjoy Dasgupta, Nave Frost, and Michal Moshkovitz. 2022. Framework for evaluating faithfulness of local explanations. In International Conference on Machine Learning. PMLR, 4794--4815."},{"key":"e_1_2_1_17_1","volume-title":"FEDEX: An Explainability Framework for Data Exploration Steps. arXiv preprint arXiv:2209.06260","author":"Deutch Daniel","year":"2022","unstructured":"Daniel Deutch, Amir Gilad, Tova Milo, Amit Mualem, and Amit Somech. 2022. FEDEX: An Explainability Framework for Data Exploration Steps. arXiv preprint arXiv:2209.06260 (2022)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3314037"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/01969727408546059"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0658-4"},{"key":"e_1_2_1_21_1","volume-title":"Interactive data analysis: The control project. Computer 32, 8","author":"Hellerstein Joseph M","year":"1999","unstructured":"Joseph M Hellerstein, Ron Avnur, Andy Chou, Christian Hidber, Chris Olston, Vijayshankar Raman, Tali Roth, and Peter J Haas. 1999. Interactive data analysis: The control project. Computer 32, 8 (1999)."},{"key":"e_1_2_1_22_1","volume-title":"Knowledge Discovery and Measures of Interest","author":"Hilderman Robert J","unstructured":"Robert J Hilderman and Howard J Hamilton. 2013. Knowledge Discovery and Measures of Interest. Vol. 638. Springer Science & Business Media."},{"key":"e_1_2_1_23_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9272-4"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/3494124.3494151"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.61115"},{"key":"e_1_2_1_28_1","volume-title":"Lundberg and Su-In Lee","author":"Scott","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Yuyu Luo Xuedi Qin Nan Tang and Guoliang Li. 2018. DeepEye: Towards Automatic Data Visualization. ICDE.","DOI":"10.1109\/ICDE.2018.00019"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1951.10500769"},{"key":"e_1_2_1_31_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NeurIPS."},{"key":"e_1_2_1_32_1","unstructured":"Christoph Molnar. 2020. Interpretable machine learning. Lulu. com."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/isaf.1422"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3520154"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125--7"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Sunita Sarawagi Rakesh Agrawal and Nimrod Megiddo. 1998. Discovery-driven exploration of OLAP data cubes. In EDBT.","DOI":"10.1007\/BFb0100984"},{"key":"e_1_2_1_38_1","volume-title":"DBExplorer: Exploratory Search in Databases. EDBT","author":"Singh Manish","year":"2016","unstructured":"Manish Singh, Michael J Cafarella, and HV Jagadish. 2016. DBExplorer: Exploratory Search in Databases. EDBT (2016)."},{"key":"e_1_2_1_39_1","volume-title":"Augmenting visualizations with interactive data facts to facilitate interpretation and communication","author":"Srinivasan Arjun","year":"2018","unstructured":"Arjun Srinivasan, Steven M Drucker, Alex Endert, and John Stasko. 2018. Augmenting visualizations with interactive data facts to facilitate interpretation and communication. IEEE transactions on visualization and computer graphics 25, 1 (2018), 672--681."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3035922"},{"key":"e_1_2_1_41_1","first-page":"18853","article-title":"Subtab: Subsetting features of tabular data for selfsupervised representation learning","volume":"34","author":"Ucar Talip","year":"2021","unstructured":"Talip Ucar, Ehsan Hajiramezanali, and Lindsay Edwards. 2021. Subtab: Subsetting features of tabular data for selfsupervised representation learning. Advances in Neural Information Processing Systems 34 (2021), 18853--18865.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_42_1","first-page":"18853","article-title":"Subtab: Subsetting features of tabular data for selfsupervised representation learning","volume":"34","author":"Ucar Talip","year":"2021","unstructured":"Talip Ucar, Ehsan Hajiramezanali, and Lindsay Edwards. 2021. Subtab: Subsetting features of tabular data for selfsupervised representation learning. Advances in Neural Information Processing Systems 34 (2021), 18853--18865.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_43_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2014.6804281"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.009"},{"key":"e_1_2_1_46_1","unstructured":"ZifengWang and Jimeng Sun. [n. d.]. TransTab: Learning Transferable Tabular Transformers Across Tables. In Advances in Neural Information Processing Systems."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1852102.1852106"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1852102.1852106"},{"key":"e_1_2_1_49_1","unstructured":"Daniel Whiteson. 2014. Higgs Boson Dataset. https:\/\/archive.ics.uci.edu\/dataset\/280\/higgs."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00309"},{"key":"e_1_2_1_51_1","volume-title":"Voyager: Exploratory analysis via faceted browsing of visualization recommendations. TVCG","author":"Moritz Dominik","year":"2016","unstructured":"KanitWongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2016. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. TVCG (2016)."},{"key":"e_1_2_1_52_1","first-page":"11033","article-title":"Vime: Extending the success of self-and semi-supervised learning to tabular domain","volume":"33","author":"Yoon Jinsung","year":"2020","unstructured":"Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar. 2020. Vime: Extending the success of self-and semi-supervised learning to tabular domain. Advances in Neural Information Processing Systems 33 (2020), 11033--11043.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538603"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2735381"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639329","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639329","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T15:17:17Z","timestamp":1755789437000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,12]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3,12]]}},"alternative-id":["10.1145\/3639329"],"URL":"https:\/\/doi.org\/10.1145\/3639329","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,12]]}}}