{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:04:39Z","timestamp":1784048679248,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Dutch Research Council &#x28;NWO&#x29;","award":["24.004.022"],"award-info":[{"award-number":["24.004.022"]}]},{"name":"Dutch Research Council &#x28;NWO&#x29;","award":["NWA.1389.20.- 183"],"award-info":[{"award-number":["NWA.1389.20.- 183"]}]},{"name":"Dutch Research Council &#x28;NWO&#x29;","award":["KICH3.LTP.20.006"],"award-info":[{"award-number":["KICH3.LTP.20.006"]}]},{"name":"Dutch Research Council &#x28;NWO&#x29;","award":["VI.Veni.222.269"],"award-info":[{"award-number":["VI.Veni.222.269"]}]},{"name":"European Union","award":["101201510 &#x28;UNITE&#x29;"],"award-info":[{"award-number":["101201510 &#x28;UNITE&#x29;"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,25]]},"DOI":"10.1145\/3805713.3820419","type":"proceedings-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:08:31Z","timestamp":1784045311000},"page":"414-424","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prior-Data Fitted Networks as Tabular Foundation Models for Ranking in Low-Data Settings"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8925-1585","authenticated-orcid":false,"given":"David","family":"Vos","sequence":"first","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5204-8514","authenticated-orcid":false,"given":"Samarth","family":"Bhargav","sequence":"additional","affiliation":[{"name":"Cohere, Amsterdam, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1086-0202","authenticated-orcid":false,"given":"Maarten de","family":"Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0458-9233","authenticated-orcid":false,"given":"Harrie","family":"Oosterhuis","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_1_2_1","unstructured":"Cristian Bodnar Wessel P. Bruinsma Ana Lucic Megan Stanley Anna Allen Johannes Brandstetter Patrick Garvan Maik Riechert Jonathan A Weyn Haiyu Dong et al. 2025. A Foundation Model for the Earth System. Nature (2025) 1-8."},{"key":"e_1_3_2_1_3_1","first-page":"21","article-title":"Deep Learning for Tabular Data: A Survey","volume":"35","author":"Borisov Vadim","year":"2022","unstructured":"Vadim Borisov, Tobias Leemann, Kathrin Se\u00dfler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci. 2022. Deep Learning for Tabular Data: A Survey. IEEE Transactions on Neural Networks and Learning Systems, Vol. 35, 1 (2022), 21-39.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_1_4_1","first-page":"23","article-title":"From Ranknet to LambdaRank to LambdaMART","volume":"11","author":"Burges Christopher JC","year":"2010","unstructured":"Christopher JC Burges. 2010. From Ranknet to LambdaRank to LambdaMART: An Overview. Learning, Vol. 11, 23-581 (2010), 81.","journal-title":"An Overview. Learning"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the Learning to Rank Challenge (Proceedings of Machine Learning Research","volume":"24","author":"Chapelle Olivier","year":"2011","unstructured":"Olivier Chapelle and Yi Chang. 2011. Yahoo! Learning to Rank Challenge Overview. In Proceedings of the Learning to Rank Challenge (Proceedings of Machine Learning Research, Vol. 14), Olivier Chapelle, Yi Chang, and Tie-Yan Liu (Eds.). PMLR, Haifa, Israel, 1-24. https:\/\/proceedings.mlr.press\/v14\/chapelle11a.html"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.500-338.deep-overview"},{"key":"e_1_3_2_1_8_1","volume-title":"Search Engines: Information Retrieval in Practice.","author":"Croft W. Bruce","year":"2010","unstructured":"W. Bruce Croft, Donald Metzler, Trevor Strohman, et al., 2010. Search Engines: Information Retrieval in Practice. Vol. 520. Addison-Wesley Reading."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987380"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531740"},{"key":"e_1_3_2_1_11_1","first-page":"4171","volume-title":"Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 4171-4186."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3578337.3605136"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1008992.1009004"},{"key":"e_1_3_2_1_14_1","volume-title":"Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, and Colin White.","author":"Feuer Benjamin","year":"2024","unstructured":"Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, and Colin White. 2024. TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. arXiv preprint arXiv:2402.11137 (2024)."},{"key":"e_1_3_2_1_15_1","volume-title":"Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models. arXiv preprint arXiv:2512.00888","author":"Gangwani Ishaan","year":"2025","unstructured":"Ishaan Gangwani and Aayam Bansal. 2025. Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models. arXiv preprint arXiv:2512.00888 (2025)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.311"},{"key":"e_1_3_2_1_17_1","unstructured":"L\u00e9o Grinsztajn Klemens Fl\u00f6ge Oscar Key Felix Birkel Philipp Jund Brendan Roof Benjamin J\u00e4ger Dominik Safaric Simone Alessi Adrian Hayler et al. 2025. TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models. arXiv preprint arXiv:2511.08667 (2025)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0037"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the Learning to Rank Challenge (Proceedings of Machine Learning Research","volume":"76","author":"Gulin Andrey","year":"2011","unstructured":"Andrey Gulin, Igor Kuralenok, and Dimitry Pavlov. 2011. Winning The Transfer Learning Track of Yahoo!'s Learning To Rank Challenge with YetiRank. In Proceedings of the Learning to Rank Challenge (Proceedings of Machine Learning Research, Vol. 14), Olivier Chapelle, Yi Chang, and Tie-Yan Liu (Eds.). PMLR, Haifa, Israel, 63-76. https:\/\/proceedings.mlr.press\/v14\/gulin11a.html"},{"key":"e_1_3_2_1_20_1","volume-title":"TabLLM: Few-shot Classification of Tabular Data with Large Language Models. In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 5549-5581","author":"Hegselmann Stefan","year":"2023","unstructured":"Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, and David Sontag. 2023. TabLLM: Few-shot Classification of Tabular Data with Large Language Models. In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 5549-5581."},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Hollmann Noah","year":"2023","unstructured":"Noah Hollmann, Samuel M\u00fcller, Katharina Eggensperger, and Frank Hutter. 2023. TabPFN: A Transformer that Solves Small Tabular Classification Problems in a Second. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s41586-024-08328-6","article-title":"Accurate Predictions on Small Tabular Data with Tabular Foundation Models","volume":"637","author":"Hollmann Noah","year":"2025","unstructured":"Noah Hollmann, Samuel G. M\u00fcller, Katharina Eggensperger, and Frank Hutter. 2025. Accurate Predictions on Small Tabular Data with Tabular Foundation Models. Nature, Vol. 637 (2025), 319-326.","journal-title":"Nature"},{"key":"e_1_3_2_1_23_1","volume-title":"From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models. arXiv preprint arXiv:2501.02945","author":"Hoo Sherrie","year":"2025","unstructured":"Sherrie Hoo, Noah Hollmann, Samuel G. M\u00fcller, and Frank Hutter. 2025. From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models. arXiv preprint arXiv:2501.02945 (2025)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539065"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_2_1_26_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1561\/1500000016"},{"key":"e_1_3_2_1_28_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","volume":"35","author":"Lorch Lars","year":"2022","unstructured":"Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, and Bernhard Sch\u00f6lkopf. 2022. Amortized Inference for Causal Structure Learning. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 35."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914763"},{"key":"e_1_3_2_1_30_1","volume-title":"In-Context Data Distillation with TabPFN. arXiv [cs.LG] (Feb","author":"Ma Junwei","year":"2024","unstructured":"Junwei Ma, Valentin Thomas, Guangwei Yu, and Anthony Caterini. 2024. In-Context Data Distillation with TabPFN. arXiv [cs.LG] (Feb. 2024)."},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"M\u00fcller Samuel","year":"2022","unstructured":"Samuel M\u00fcller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. 2022. Transformers can do Bayesian inference. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_32_1","volume-title":"Statistical Foundations of Prior-data Fitted Networks. In International Conference on Machine Learning (ICML). PMLR, 25660-25676","author":"Nagler Thomas","year":"2023","unstructured":"Thomas Nagler. 2023. Statistical Foundations of Prior-data Fitted Networks. In International Conference on Machine Learning (ICML). PMLR, 25660-25676."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462830"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271686"},{"key":"e_1_3_2_1_35_1","first-page":"6638","article-title":"CatBoost: Unbiased Boosting with Categorical Features","volume":"31","author":"Prokhorenkova Liudmila","year":"2018","unstructured":"Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: Unbiased Boosting with Categorical Features. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 31. 6638-6648.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_3_2_1_36_1","volume-title":"arXiv preprint arXiv:1306.2597","author":"Qin Tao","year":"2013","unstructured":"Tao Qin and Tie-Yan Liu. 2013. Introducing LETOR 4.0 datasets. arXiv preprint arXiv:1306.2597 (2013)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-naacl.97"},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Qin Zhen","year":"2021","unstructured":"Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2021. Are Neural Rankers Still Outperformed by Gradient Boosted Decision Trees?. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_39_1","volume-title":"TabICL: A Tabular Foundation Model for in-Context Learning on Large Data. arXiv [cs.LG] (Feb","author":"Qu Jingang","year":"2025","unstructured":"Jingang Qu, David Holzm\u00fcller, Ga\u00ebl Varoquaux, and Marine Le Morvan. 2025. TabICL: A Tabular Foundation Model for in-Context Learning on Large Data. arXiv [cs.LG] (Feb. 2025)."},{"key":"e_1_3_2_1_40_1","volume-title":"Do-PFN: In-Context Learning for Causal Effect Estimation. In International Conference on Learning Representations (ICLR).","author":"Robertson Jake","year":"2025","unstructured":"Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, and Bernhard Sch\u00f6lkopf. 2025. Do-PFN: In-Context Learning for Causal Effect Estimation. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835928"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.11.011"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.54195\/irrj.19625"},{"key":"e_1_3_2_1_44_1","volume-title":"The Probable Error of a Mean. Biometrika","year":"1908","unstructured":"Student. 1908. The Probable Error of a Mean. Biometrika (1908), 1-25."},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). 14914-14937","author":"Sun Weiwei","year":"2023","unstructured":"Weiwei Sun, Lingyong Yan, Zheng Ma, Pengjie Ren, Zhumin Chen, and Zhaochun Ren. 2023. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). 14914-14937."},{"key":"e_1_3_2_1_46_1","unstructured":"Nandan Thakur Nils Reimers Andreas R\u00fcckl\u00e9 Abhishek Srivastava and Iryna Gurevych. 2021. BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). https:\/\/openreview.net\/forum?id=wCu6T5xFjeJ"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657707"},{"key":"e_1_3_2_1_48_1","unstructured":"Maksims Volkovs Yves Raimond David Steiner and Et al. 2024. Retrieval & Fine-Tuning for In-Context Tabular Models. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3471158.3472236"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3641960"},{"key":"e_1_3_2_1_51_1","first-page":"2902","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Wang Zifeng","year":"2022","unstructured":"Zifeng Wang and Jimeng Sun. 2022. TransTab: Learning Transferable Tabular Transformers Across Tables. In Advances in Neural Information Processing Systems, Vol. 35. Curran Associates, Inc., 2902-2915. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/1377f76686d56439a2bd7a91859972f5-Paper-Conference.pdf"},{"key":"e_1_3_2_1_52_1","volume-title":"The 4th Table Representation Learning Workshop at ACL","author":"Wolff Cornelius","year":"2025","unstructured":"Cornelius Wolff and Madelon Hulsebos. 2025. How Well Do LLMs Reason over Tabular Data, Really?. In The 4th Table Representation Learning Workshop at ACL 2025."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3698876"},{"key":"e_1_3_2_1_54_1","unstructured":"An Yang Anfeng Li Baosong Yang Beichen Zhang Binyuan Hui Bo Zheng Bowen Yu Chang Gao Chengen Huang Chenxu Lv et al. 2025. Qwen3 Technical Report. arXiv preprint arXiv:2505.09388 (2025)."},{"key":"e_1_3_2_1_55_1","volume-title":"Mueller","author":"Zeng Yuchen","year":"2025","unstructured":"Yuchen Zeng, Tuan Dinh, Wonjun Kang, and Andreas C. Mueller. 2025. Tabflex: Scaling Tabular Learning to Millions with Linear Attention. arXiv preprint arXiv:2506.05584 (2025)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/515"}],"event":{"name":"ICTIR '26: International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 2026 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)"],"original-title":[],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:08:58Z","timestamp":1784045338000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805713.3820419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":56,"alternative-id":["10.1145\/3805713.3820419","10.1145\/3805713"],"URL":"https:\/\/doi.org\/10.1145\/3805713.3820419","relation":{},"subject":[],"published":{"date-parts":[[2026,7,24]]},"assertion":[{"value":"2026-07-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}