[{"intvolume":"       220","date_updated":"2025-11-04T12:25:54Z","author":[{"full_name":"Roberts, Nicholas","last_name":"Roberts","first_name":"Nicholas"},{"last_name":"Guo","first_name":"Samuel","full_name":"Guo, Samuel"},{"first_name":"Cong","last_name":"Xu","full_name":"Xu, Cong"},{"first_name":"Ameet","last_name":"Talwalkar","full_name":"Talwalkar, Ameet"},{"full_name":"Lander, David","first_name":"David","last_name":"Lander"},{"full_name":"Tao, Lvfang","first_name":"Lvfang","last_name":"Tao"},{"full_name":"Cai, Linhang","first_name":"Linhang","last_name":"Cai"},{"last_name":"Niu","first_name":"Shuaicheng","full_name":"Niu, Shuaicheng"},{"last_name":"Heng","first_name":"Jianyu","full_name":"Heng, Jianyu"},{"full_name":"Qin, Hongyang","last_name":"Qin","first_name":"Hongyang"},{"full_name":"Deng, Minwen","last_name":"Deng","first_name":"Minwen"},{"full_name":"Hog, Johannes","last_name":"Hog","first_name":"Johannes"},{"full_name":"Pfefferle, Alexander","last_name":"Pfefferle","first_name":"Alexander"},{"full_name":"Ammanaghatta Shivakumar, Sushil","first_name":"Sushil","last_name":"Ammanaghatta Shivakumar"},{"last_name":"Krishnakumar","first_name":"Arjun","full_name":"Krishnakumar, Arjun"},{"full_name":"Wang, Yubo","first_name":"Yubo","last_name":"Wang"},{"full_name":"Sukthanker, Rhea","last_name":"Sukthanker","first_name":"Rhea"},{"first_name":"Frank","last_name":"Hutter","full_name":"Hutter, Frank"},{"full_name":"Hasanaj, Euxhen","last_name":"Hasanaj","first_name":"Euxhen"},{"first_name":"Tien-Dung","last_name":"Le","full_name":"Le, Tien-Dung"},{"full_name":"Khodak, Mikhail","last_name":"Khodak","first_name":"Mikhail"},{"last_name":"Nevmyvaka","first_name":"Yuriy","full_name":"Nevmyvaka, Yuriy"},{"last_name":"Rasul","first_name":"Kashif","full_name":"Rasul, Kashif"},{"last_name":"Sala","first_name":"Frederic","full_name":"Sala, Frederic"},{"last_name":"Schneider","first_name":"Anderson","full_name":"Schneider, Anderson"},{"full_name":"Shen, Junhong","first_name":"Junhong","last_name":"Shen"},{"full_name":"Sparks, Evan","last_name":"Sparks","first_name":"Evan"}],"title":"AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale","year":"2022","status":"public","editor":[{"first_name":"Marco","last_name":"Ciccone","full_name":"Ciccone, Marco"},{"full_name":"Stolovitzky, Gustavo","last_name":"Stolovitzky","first_name":"Gustavo"},{"full_name":"Albrecht, Jacob","first_name":"Jacob","last_name":"Albrecht"}],"volume":220,"user_id":"124732","series_title":"Proceedings of Machine Learning Research","_id":"62072","language":[{"iso":"eng"}],"publisher":"PMLR","page":"151–170","abstract":[{"text":"The vision of Automated Machine Learning (AutoML) is to produce high performing ML pipelines that require very little human involvement or domain expertise to use. Competitions and benchmarks have been critical tools for accelerating progress in AutoML. However, much of the prior work on AutoML competitions has focused on well-studied domains in machine learning such as vision and language—these are domains which have benefited from several years of ML pipeline design by domain experts, which brings the usage of AutoML into question in the first place. Recently, AutoML for diverse tasks has emerged as an important research area that aims to bring AutoML to the domains where it can have the most impact: the long tail of ML tasks <em>beyond vision and language</em>. We present a retrospective report of the AutoML Decathlon—an AutoML for diverse tasks competition hosted at NeurIPS 2022. The AutoML Decathlon presented participants with a set of 10 machine learning tasks that are diverse along several axes: domain, input dimension, output dimension, output type, objective function, and scale. Participants were tasked with developing AutoML methods that performed well on a <em>separate</em> set of 10 hidden diverse test tasks within a certain time budget, so as to discourage overfitting to the initial set of tasks and to encourage efficiency. In this report, we outline the details of the competition, discuss the top-5 submissions, analyze the results, and compare top submissions to additional state-of-the-art baselines designed specifically for diverse tasks. We conclude that the combination of existing efficient AutoML techniques with modern advancements in ML such as large-scale transfer learning, modern architectures, and differentiable Neural Architecture Search (NAS) is a promising direction for AutoML for diverse tasks.","lang":"eng"}],"extern":"1","citation":{"ieee":"N. Roberts <i>et al.</i>, “AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale,” in <i>Proceedings of the NeurIPS 2022 Competitions Track</i>, 2022, vol. 220, pp. 151–170.","apa":"Roberts, N., Guo, S., Xu, C., Talwalkar, A., Lander, D., Tao, L., Cai, L., Niu, S., Heng, J., Qin, H., Deng, M., Hog, J., Pfefferle, A., Ammanaghatta Shivakumar, S., Krishnakumar, A., Wang, Y., Sukthanker, R., Hutter, F., Hasanaj, E., … Sparks, E. (2022). AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. In M. Ciccone, G. Stolovitzky, &#38; J. Albrecht (Eds.), <i>Proceedings of the NeurIPS 2022 Competitions Track</i> (Vol. 220, pp. 151–170). PMLR.","chicago":"Roberts, Nicholas, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, et al. “AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale.” In <i>Proceedings of the NeurIPS 2022 Competitions Track</i>, edited by Marco Ciccone, Gustavo Stolovitzky, and Jacob Albrecht, 220:151–170. Proceedings of Machine Learning Research. PMLR, 2022.","short":"N. Roberts, S. Guo, C. Xu, A. Talwalkar, D. Lander, L. Tao, L. Cai, S. Niu, J. Heng, H. Qin, M. Deng, J. Hog, A. Pfefferle, S. Ammanaghatta Shivakumar, A. Krishnakumar, Y. Wang, R. Sukthanker, F. Hutter, E. Hasanaj, T.-D. Le, M. Khodak, Y. Nevmyvaka, K. Rasul, F. Sala, A. Schneider, J. Shen, E. Sparks, in: M. Ciccone, G. Stolovitzky, J. Albrecht (Eds.), Proceedings of the NeurIPS 2022 Competitions Track, PMLR, 2022, pp. 151–170.","mla":"Roberts, Nicholas, et al. “AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale.” <i>Proceedings of the NeurIPS 2022 Competitions Track</i>, edited by Marco Ciccone et al., vol. 220, PMLR, 2022, pp. 151–170.","bibtex":"@inproceedings{Roberts_Guo_Xu_Talwalkar_Lander_Tao_Cai_Niu_Heng_Qin_et al._2022, series={Proceedings of Machine Learning Research}, title={AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale}, volume={220}, booktitle={Proceedings of the NeurIPS 2022 Competitions Track}, publisher={PMLR}, author={Roberts, Nicholas and Guo, Samuel and Xu, Cong and Talwalkar, Ameet and Lander, David and Tao, Lvfang and Cai, Linhang and Niu, Shuaicheng and Heng, Jianyu and Qin, Hongyang and et al.}, editor={Ciccone, Marco and Stolovitzky, Gustavo and Albrecht, Jacob}, year={2022}, pages={151–170}, collection={Proceedings of Machine Learning Research} }","ama":"Roberts N, Guo S, Xu C, et al. AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. In: Ciccone M, Stolovitzky G, Albrecht J, eds. <i>Proceedings of the NeurIPS 2022 Competitions Track</i>. Vol 220. Proceedings of Machine Learning Research. PMLR; 2022:151–170."},"publication":"Proceedings of the NeurIPS 2022 Competitions Track","type":"conference","date_created":"2025-11-04T12:24:24Z"}]