{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:24:44Z","timestamp":1768807484728,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62250069"],"award-info":[{"award-number":["62250069"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671617","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"5773-5782","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Beimingwu: A Learnware Dock System"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4607-6089","authenticated-orcid":false,"given":"Zhi-Hao","family":"Tan","sequence":"first","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1753-4680","authenticated-orcid":false,"given":"Jian-Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9181-1286","authenticated-orcid":false,"given":"Xiao-Dong","family":"Bi","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3749-9266","authenticated-orcid":false,"given":"Peng","family":"Tan","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8762-3807","authenticated-orcid":false,"given":"Qin-Cheng","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9952-5621","authenticated-orcid":false,"given":"Hai-Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7773-7738","authenticated-orcid":false,"given":"Yi","family":"Xie","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6802-8996","authenticated-orcid":false,"given":"Xiao-Chuan","family":"Zou","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6824-1480","authenticated-orcid":false,"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0746-1494","authenticated-orcid":false,"given":"Zhi-Hua","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University &amp; School of Artificial Intelligence, Nanjing University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Shai Ben-David John Blitzer Koby Crammer and Fernando Pereira. 2006. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems 19.","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"e_1_3_2_2_2_1","volume-title":"Reproducing kernel Hilbert spaces in probability and statistics","author":"Berlinet Alain","unstructured":"Alain Berlinet and Christine Thomas-Agnan. 2011. Reproducing kernel Hilbert spaces in probability and statistics. Springer Science & Business Media."},{"key":"e_1_3_2_2_3_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel Ziegler Jeffrey Wu Clemens Winter Chris Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems 33. 1877--1901."},{"key":"e_1_3_2_2_4_1","unstructured":"Yao-Xiang Ding Xi-Zhu Wu Kun Zhou and Zhi-Hua Zhou. 2022. Pre-trained model reusability evaluation for small-data transfer learning. In Advances in Neural Information Processing Systems 35. 37389--37400."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553411"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3618408.3618896"},{"key":"e_1_3_2_2_7_1","unstructured":"Arthur Jacot Cl\u00e9ment Hongler and Franck Gabriel. 2018. Neural tangent kernel: convergence and generalization in neural networks. In Advances in Neural Information Processing Systems 31. 8580--8589."},{"key":"e_1_3_2_2_8_1","volume-title":"Proceedings of the 14th International Conference on Machine Learning. 143--151","author":"Joachims Thorsten","year":"1997","unstructured":"Thorsten Joachims. 1997. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In Proceedings of the 14th International Conference on Machine Learning. 143--151."},{"key":"e_1_3_2_2_9_1","unstructured":"Kaggle. 2017. Corporacion favorita grocery sales forecasting. https:\/\/www.kaggle.com\/c\/favorita-grocery-sales-forecasting. Accessed: 2023-06-20."},{"key":"e_1_3_2_2_10_1","unstructured":"Kaggle. 2018. Predict future sales. https:\/\/kaggle.com\/competitions\/competitive-data-science-predict-future-sales. Accessed: 2023-05-20."},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning. 942--950","author":"Kuzborskij Ilja","year":"2013","unstructured":"Ilja Kuzborskij and Francesco Orabona. 2013. Stability and hypothesis transfer learning. In Proceedings of the 30th International Conference on Machine Learning. 942--950."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.172"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29298"},{"key":"e_1_3_2_2_15_1","volume-title":"Song-Chun Zhu, and Jianfeng Gao.","author":"Lu Pan","year":"2023","unstructured":"Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023. Chameleon: Plug-and-play compositional reasoning with large language models. In Advances in Neural Information Processing Systems 36. 43447--43478."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.07.007"},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning. 7294--7305","author":"Nguyen Cuong","year":"2020","unstructured":"Cuong Nguyen, Tal Hassner, Matthias Seeger, and Cedric Archambeau. 2020. Leep: A new measure to evaluate transferability of learned representations. In Proceedings of the 37th International Conference on Machine Learning. 7294--7305."},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning. 17018--17044","author":"Novak Roman","unstructured":"Roman Novak, Jascha Sohl-Dickstein, and Samuel S. Schoenholz. 2022. Fast finite width neural tangent kernel. In Proceedings of the 39th International Conference on Machine Learning. 17018--17044."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_2_21_1","volume-title":"Gorilla: Large language model connected with massive apis. arXiv preprint arXiv:2305.15334","author":"Patil Shishir G","year":"2023","unstructured":"Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. 2023. Gorilla: Large language model connected with massive apis. arXiv preprint arXiv:2305.15334 (2023)."},{"key":"e_1_3_2_2_22_1","volume-title":"2021 USENIX Annual Technical Conference. 397--411","author":"Romero Francisco","year":"2021","unstructured":"Francisco Romero, Qian Li, Neeraja J Yadwadkar, and Christos Kozyrakis. 2021. INFaaS: Automated model-less inference serving. In 2021 USENIX Annual Technical Conference. 397--411."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.788641"},{"key":"e_1_3_2_2_24_1","volume-title":"Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. In Advances in Neural Information Processing Systems 36. 38154--38180.","author":"Shen Yongliang","year":"2023","unstructured":"Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. In Advances in Neural Information Processing Systems 36. 38154--38180."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/471"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06245-1"},{"key":"e_1_3_2_2_27_1","first-page":"1232","article-title":"Learnware reduced kernel mean embedding specification based on neural tangent kernel","volume":"47","author":"Tan Zhi-Hao","year":"2024","unstructured":"Zhi-Hao Tan, Hao-Yu Shi, Zi-Xuan Chen, and Jiang Yuan. 2024. Learnware reduced kernel mean embedding specification based on neural tangent kernel. Chinese Journal of Computers, Vol. 47, 6 (2024), 1232--1243.","journal-title":"Chinese Journal of Computers"},{"key":"e_1_3_2_2_28_1","unstructured":"Zhi-Hao Tan Yi Xie Yuan Jiang and Zhi-Hua Zhou. 2022. Real-valued backpropagation is unsuitable for complex-valued neural networks. In Advances in Neural Information Processing Systems 35. 34052--34063."},{"key":"e_1_3_2_2_29_1","volume-title":"Transtab: Learning transferable tabular transformers across tables. In Advances in Neural Information Processing Systems 35. 2902--2915.","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 35. 2902--2915."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3086619"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-14714-2_30"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA230585"},{"key":"e_1_3_2_2_33_1","unstructured":"Shiqi Yang Yaxing Wang Kai Wang Shangling Jui and Joost van de Weijer. 2022. Attracting and dispersing: A simple approach for source-free domain adaptation. In Advances in Neural Information Processing Systems 35. 5802--5815."},{"key":"e_1_3_2_2_34_1","first-page":"1","article-title":"Ranking and tuning pre-trained models: a new paradigm for exploiting model hubs","volume":"23","author":"You Kaichao","year":"2022","unstructured":"Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I Jordan, and Mingsheng Long. 2022. Ranking and tuning pre-trained models: a new paradigm for exploiting model hubs. Journal of Machine Learning Research, Vol. 23, 209 (2022), 1--47.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17309"},{"key":"e_1_3_2_2_36_1","unstructured":"Yi-Kai Zhang Ting-Ji Huang Yao-Xiang Ding De-Chuan Zhan and Han-Jia Ye. 2023. Model spider: learning to rank pre-trained models efficiently. In Advances in Neural Information Processing Systems 36. 13692--13719."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05835-w"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00135"},{"key":"e_1_3_2_2_39_1","volume-title":"Ensemble methods: foundations and algorithms","author":"Zhou Zhi-Hua","unstructured":"Zhi-Hua Zhou. 2012. Ensemble methods: foundations and algorithms. CRC press."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-016-6906-3"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3823-6"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671617","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671617","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:59Z","timestamp":1750291559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671617"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":41,"alternative-id":["10.1145\/3637528.3671617","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671617","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}