{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:13:09Z","timestamp":1771330389560,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":31,"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"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671894","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"2411-2419","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["CASH via Optimal Diversity for Ensemble Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0673-4397","authenticated-orcid":false,"given":"Pranav","family":"Poduval","sequence":"first","affiliation":[{"name":"MasterCard AI Garage, Gurgaon, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6766-640X","authenticated-orcid":false,"given":"Sanjay Kumar","family":"Patnala","sequence":"additional","affiliation":[{"name":"Mastercard AI Garage, Gurgaon, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3621-0893","authenticated-orcid":false,"given":"Gaurav","family":"Oberoi","sequence":"additional","affiliation":[{"name":"MasterCard AI Garage, Gurgaon, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1195-8308","authenticated-orcid":false,"given":"Nitish","family":"Srivasatava","sequence":"additional","affiliation":[{"name":"MasterCard AI Garage, Gurgaon, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-1240","authenticated-orcid":false,"given":"Siddhartha","family":"Asthana","sequence":"additional","affiliation":[{"name":"MasterCard AI Garage, Gurgaon, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Taiga Abe E Kelly Buchanan Geoff Pleiss and John Patrick Cunningham. 2022. The best deep ensembles sacrifice predictive diversity. In I Can't Believe It's Not Better Workshop: Understanding Deep Learning Through Empirical Falsification."},{"key":"e_1_3_2_2_2_1","volume-title":"Algorithms for hyper-parameter optimization. Advances in neural information processing systems 24","author":"Bergstra James","year":"2011","unstructured":"James Bergstra, R\u00e9mi Bardenet, Yoshua Bengio, and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for hyper-parameter optimization. Advances in neural information processing systems 24 (2011)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3053165"},{"key":"e_1_3_2_2_4_1","volume-title":"Stacked regressions. Machine learning 24","author":"Breiman Leo","year":"1996","unstructured":"Leo Breiman. 1996. Stacked regressions. Machine learning 24 (1996), 49--64."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/648054.743935"},{"key":"e_1_3_2_2_6_1","volume-title":"Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505","author":"Erickson Nick","year":"2020","unstructured":"Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola. 2020. Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505 (2020)."},{"key":"e_1_3_2_2_7_1","volume-title":"Efficient and robust automated machine learning. Advances in neural information processing systems 28","author":"Feurer Matthias","year":"2015","unstructured":"Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015. Efficient and robust automated machine learning. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.58871"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","DOI":"10.1109\/CDC.2001.980896","volume-title":"Proceedings of the 40th IEEE conference on decision and control (Cat. No. 01CH37228)","volume":"5","author":"Ho Yu-Chi","year":"2001","unstructured":"Yu-Chi Ho and David L Pepyne. 2001. Simple explanation of the no free lunch theorem of optimization. In Proceedings of the 40th IEEE conference on decision and control (Cat. No. 01CH37228), Vol. 5. IEEE, 4409--4414."},{"key":"e_1_3_2_2_11_1","unstructured":"Thomas Hoch. 2015. An Ensemble Learning Approach for the Kaggle Taxi Travel Time Prediction Challenge.. In DC@ PKDD\/ECML."},{"key":"e_1_3_2_2_12_1","volume-title":"Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine learning 51","author":"Kuncheva Ludmila I","year":"2003","unstructured":"Ludmila I Kuncheva and Christopher J Whitaker. 2003. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine learning 51 (2003), 181--207."},{"key":"e_1_3_2_2_13_1","volume-title":"Bayesian hyperparameter optimization for ensemble learning. arXiv preprint arXiv:1605.06394","author":"L\u00e9vesque Julien-Charles","year":"2016","unstructured":"Julien-Charles L\u00e9vesque, Christian Gagn\u00e9, and Robert Sabourin. 2016. Bayesian hyperparameter optimization for ensemble learning. arXiv preprint arXiv:1605.06394 (2016)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5910"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467061"},{"key":"e_1_3_2_2_16_1","unstructured":"Yang Li Yu Shen Wentao Zhang Jiawei Jiang Bolin Ding Yaliang Li Jingren Zhou Zhi Yang Wentao Wu Ce Zhang et al. 2021. VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition. arXiv preprint arXiv:2107.08861 (2021)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5926"},{"key":"e_1_3_2_2_18_1","volume-title":"d.]. Uncertainty in gradient boosting via ensembles. arXiv","author":"Malinin A","year":"2020","unstructured":"A Malinin, L Prokhorenkova, and A Ustimenko. [n. d.]. Uncertainty in gradient boosting via ensembles. arXiv 2020. arXiv preprint arXiv:2006.10562 ([n. d.])."},{"key":"e_1_3_2_2_19_1","first-page":"5263","article-title":"Second order PAC-Bayesian bounds for the weighted majority vote","volume":"33","author":"Masegosa Andr\u00e9s","year":"2020","unstructured":"Andr\u00e9s Masegosa, Stephan Lorenzen, Christian Igel, and Yevgeny Seldin. 2020. Second order PAC-Bayesian bounds for the weighted majority vote. Advances in Neural Information Processing Systems 33 (2020), 5263--5273.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_20_1","volume-title":"Workshop on automatic machine learning. PMLR, 66--74","author":"Olson Randal S","year":"2016","unstructured":"Randal S Olson and Jason H Moore. 2016. TPOT: A tree-based pipeline optimization tool for automating machine learning. In Workshop on automatic machine learning. PMLR, 66--74."},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 11720--11743","author":"Ortega Luis A","year":"2022","unstructured":"Luis A Ortega, Rafael Caba\u00f1as, and Andres Masegosa. 2022. Diversity and generalization in neural network ensembles. In International Conference on Artificial Intelligence and Statistics. PMLR, 11720--11743."},{"key":"e_1_3_2_2_22_1","first-page":"2958","article-title":"DivBO: Diversity-aware CASH for Ensemble Learning","volume":"35","author":"Shen Yu","year":"2022","unstructured":"Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, and Bin Cui. 2022. DivBO: Diversity-aware CASH for Ensemble Learning. Advances in Neural Information Processing Systems 35 (2022), 2958--2971.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_23_1","volume-title":"Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25","author":"Snoek Jasper","year":"2012","unstructured":"Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_2_24_1","volume-title":"A survey of collaborative filtering techniques. Advances in artificial intelligence 2009","author":"Su Xiaoyuan","year":"2009","unstructured":"Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009 (2009)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"e_1_3_2_2_26_1","volume-title":"Gradient Boosting Performs Gaussian Process Inference. In The Eleventh International Conference on Learning Representations.","author":"Ustimenko Aleksei","year":"2022","unstructured":"Aleksei Ustimenko, Artem Beliakov, and Liudmila Prokhorenkova. 2022. Gradient Boosting Performs Gaussian Process Inference. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2641190.2641198"},{"key":"e_1_3_2_2_28_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, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_29_1","first-page":"7898","article-title":"Neural ensemble search for uncertainty estimation and dataset shift","volume":"34","author":"Zaidi Sheheryar","year":"2021","unstructured":"Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C Holmes, Frank Hutter, and Yee Teh. 2021. Neural ensemble search for uncertainty estimation and dataset shift. Advances in Neural Information Processing Systems 34 (2021), 7898--7911.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00014"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3067763"}],"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\/3637528.3671894","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671894","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:15Z","timestamp":1750291455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":31,"alternative-id":["10.1145\/3637528.3671894","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671894","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"}}]}}