{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T19:22:49Z","timestamp":1787599369394,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032376534","type":"print"},{"value":"9783032376541","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-37654-1_17","type":"book-chapter","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T18:47:28Z","timestamp":1787597248000},"page":"286-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How Far Can We Compress? Attention-Guided Dataset Distillation for\u00a0TabPFN"],"prefix":"10.1007","author":[{"given":"Tao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guokun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"17_CR1","unstructured":"Bischl, B., et al.: OpenML benchmarking suites. In: Proceedings of the NeurIPS 2021 Datasets and Benchmarks Track (2021)"},{"issue":"2","key":"17_CR2","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tut. 18(2), 1153\u20131176 (2016). https:\/\/doi.org\/10.1109\/COMST.2015.2494502","journal-title":"IEEE Commun. Surv. Tut."},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Huang, W., Weng, T.W.: Provable and efficient dataset distillation for kernel ridge regression. In: Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS \u201924. Curran Associates Inc, Red Hook, NY, USA (2024)","DOI":"10.52202\/079017-2816"},{"key":"17_CR4","unstructured":"Clements, J.M., Xu, D., Yousefi, N., Efimov, D.: Sequential deep learning for credit risk monitoring with tabular financial data. arXiv arXiv:abs\/2012.15330 (2020). https:\/\/api.semanticscholar.org\/CorpusID:229924306"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Du, J., Jiang, Y., Tan, V.Y.F., Zhou, J.T., Li, H.: Minimizing the accumulated trajectory error to improve dataset distillation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3749\u20133758 (2022). https:\/\/api.semanticscholar.org\/CorpusID:253734779","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"17_CR6","unstructured":"Garg, A., Ali, M., Hollmann, N., Purucker, L., M\u00fcller, S., Hutter, F.: Real-tabPFN: improving tabular foundation models via continued pre-training with real-world data. In: 1st ICML Workshop on Foundation Models for Structured Data (2025). https:\/\/openreview.net\/forum?id=BtEiqKsIMw"},{"key":"17_CR7","unstructured":"Guo, Z., Wang, K., Cazenavette, G., Li, H., Zhang, K., You, Y.: Towards lossless dataset distillation via difficulty-aligned trajectory matching. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"17_CR8","unstructured":"Hollmann, N., M\u00fcller, S., Eggensperger, K., Hutter, F.: TabPFN: a transformer that solves small tabular classification problems in a second. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=cp5PvcI6w8_"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Hollmann, N., M\u00fcller, S., Purucker, L., Krishnakumar, A., K\u00f6rfer, M., Hoo, S.B., Schirrmeister, R.T., Hutter, F.: Accurate predictions on small data with a tabular foundation model. Nature (2025). https:\/\/doi.org\/10.1038\/s41586-024-08328-6, https:\/\/www.nature.com\/articles\/s41586-024-08328-6","DOI":"10.1038\/s41586-024-08328-6"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Kang, I., Ram, P., Zhou, Y., Samulowitz, H., Seneviratne, O.: Effective data distillation for tabular datasets (student abstract). Proceedings of the AAAI Conference on Artificial Intelligence 38(21), 23533\u201323534 (2024)","DOI":"10.1609\/aaai.v38i21.30460"},{"key":"17_CR11","unstructured":"Kim, M.J., Lefebvre, F., Brison, G., Perez-Lebel, A., Varoquaux, G.: Table foundation models: on knowledge pre-training for tabular learning. arXiv arXiv:2505.14415 (2025). https:\/\/api.semanticscholar.org\/CorpusID:278768904"},{"key":"17_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014). https:\/\/api.semanticscholar.org\/CorpusID:6628106"},{"key":"17_CR13","unstructured":"Lee, S., Chun, S., Jung, S., Yun, S., Yoon, S.: Dataset condensation with contrastive signals. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 12352\u201312364 (2022)"},{"key":"17_CR14","unstructured":"Ma, J., Thomas, V., Yu, G., Caterini, A.L.: In-context data distillation with tabPFN. In: ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models (2024). https:\/\/openreview.net\/forum?id=EkEFVhe5Gj"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"McElfresh, D., et al.: When do neural nets outperform boosted trees on tabular data? In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems, vol.\u00a036, pp. 76336\u201376369. Curran Associates, Inc. (2023)","DOI":"10.52202\/075280-3337"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Medvedev, D., D\u2019yakonov, A.: New properties of the data distillation method when working with tabular data. In: Analysis of Images, Social Networks and Texts, pp. 379\u2013390 (2021)","DOI":"10.1007\/978-3-030-72610-2_29"},{"key":"17_CR17","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=l-PrrQrK0QR"},{"key":"17_CR18","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc. (2019). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf"},{"key":"17_CR19","unstructured":"Qu, J., Holzm\u00fcller, D., Varoquaux, G., Le Morvan, M.: TabICL: a tabular foundation model for in-context learning on large data. In: International Conference on Machine Learning (2025)"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Sajedi, A., Khaki, S., Amjadian, E., Liu, L.Z., Lawryshyn, Y.A., Plataniotis, K.N.: DataDAM: efficient dataset distillation with attention matching. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 17051\u201317061 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01568","DOI":"10.1109\/ICCV51070.2023.01568"},{"issue":"8","key":"17_CR21","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1093\/europace\/euaa377","volume":"23","author":"S Somani","year":"2021","unstructured":"Somani, S., et al.: Deep learning and the electrocardiogram: review of the current state-of-the-art. EP Europace 23(8), 1179\u20131191 (2021). https:\/\/doi.org\/10.1093\/europace\/euaa377","journal-title":"EP Europace"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: CAFE: learning to condense dataset by aligning features. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12186\u201312195 (2022). https:\/\/api.semanticscholar.org\/CorpusID:263797022","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"17_CR23","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation (2020). https:\/\/arxiv.org\/abs\/1811.10959"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Sun, J.: TransTab: learning transferable tabular transformers across tables. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 2902\u20132915 (2022)","DOI":"10.52202\/068431-0210"},{"key":"17_CR25","unstructured":"Zhang, X., et al.: Mitra: mixed synthetic priors for enhancing tabular foundation models. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2026). https:\/\/openreview.net\/forum?id=t8YRsWY6HM"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with distribution matching. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6514\u20136523 (2023)","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"17_CR27","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=mSAKhLYLSsl"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, G., Li, G., Qin, Y., Yu, Y.: Improved distribution matching for dataset condensation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7856\u20137865 (2023). https:\/\/api.semanticscholar.org\/CorpusID:259982439","DOI":"10.1109\/CVPR52729.2023.00759"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Nezhadarya, E., Ba, J.: Dataset distillation using neural feature regression. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 9813\u20139827. Curran Associates, Inc. (2022). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/3fe2a777282299ecb4f9e7ebb531f0ab-Paper-Conference.pdf","DOI":"10.52202\/068431-0713"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37654-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T18:47:31Z","timestamp":1787597251000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37654-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,25]]},"ISBN":["9783032376534","9783032376541"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37654-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,25]]},"assertion":[{"value":"25 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Naples","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2026\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}