| Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019 | 2288 | 2019 |
| Taking Human out of Learning Applications: A Survey on Automated Machine Learning YY Quanming Yao, Mengshuo Wang, Yuqiang Chen, Wenyuan Dai, Yu-Feng Li, Wei ... https://arxiv.org/abs/1810.13306, 2018 | 588* | 2018 |
| Analysis of the automl challenge series I Guyon, L Sun-Hosoya, M Boullé, HJ Escalante, S Escalera, Z Liu, ... Automated Machine Learning 177, 177-219, 2019 | 167 | 2019 |
| Autocross: Automatic feature crossing for tabular data in real-world applications Y Luo, M Wang, H Zhou, Q Yao, WW Tu, Y Chen, W Dai, Q Yang Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 114 | 2019 |
| Efficient neural architecture search via proximal iterations Q Yao, J Xu, WW Tu, Z Zhu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6664-6671, 2020 | 113 | 2020 |
| Search to aggregate neighborhood for graph neural network Z Huan, YAO Quanming, TU Weiwei 2021 IEEE 37th International Conference on Data Engineering (ICDE), 552-563, 2021 | 111 | 2021 |
| Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform Z Xu, S Escalera, A Pavão, M Richard, WW Tu, Q Yao, H Zhao, I Guyon Patterns 3 (7), 2022 | 70 | 2022 |
| Robust long-tailed learning under label noise T Wei, JX Shi, WW Tu, YF Li arXiv preprint arXiv:2108.11569, 2021 | 55 | 2021 |
| Sadam: A variant of adam for strongly convex functions G Wang, S Lu, W Tu, L Zhang arXiv preprint arXiv:1905.02957, 2019 | 50 | 2019 |
| Network on network for tabular data classification in real-world applications Y Luo, H Zhou, WW Tu, Y Chen, W Dai, Q Yang Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 38 | 2020 |
| Multi-fidelity automatic hyper-parameter tuning via transfer series expansion YQ Hu, Y Yu, WW Tu, Q Yang, Y Chen, W Dai Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3846-3853, 2019 | 38 | 2019 |
| Automated 3d pre-training for molecular property prediction X Wang, H Zhao, W Tu, Q Yao Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 36 | 2023 |
| Towards automated semi-supervised learning YF Li, H Wang, T Wei, WW Tu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4237-4244, 2019 | 35 | 2019 |
| Projection-free Distributed Online Convex Optimization with $ O (\sqrtT) $ Communication Complexity Y Wan, WW Tu, L Zhang International conference on machine learning, 9818-9828, 2020 | 33 | 2020 |
| Towards AutoML in the presence of Drift: first results JG Madrid, HJ Escalante, EF Morales, WW Tu, Y Yu, L Sun-Hosoya, ... arXiv preprint arXiv:1907.10772, 2019 | 33 | 2019 |
| Chalearn LAP challenges on self-reported personality recognition and non-verbal behavior forecasting during social dyadic interactions: Dataset, design, and results C Palmero, G Barquero, JCSJ Junior, A Clapés, J Núnez, D Curto, ... Understanding Social Behavior in Dyadic and Small Group Interactions, 4-52, 2022 | 31 | 2022 |
| Tabgnn: Multiplex graph neural network for tabular data prediction X Guo, Y Quan, H Zhao, Q Yao, Y Li, W Tu arXiv preprint arXiv:2108.09127, 2021 | 30 | 2021 |
| Didn’t see that coming: a survey on non-verbal social human behavior forecasting G Barquero, J Núnez, S Escalera, Z Xu, WW Tu, I Guyon, C Palmero Understanding Social Behavior in Dyadic and Small Group Interactions, 139-178, 2022 | 27 | 2022 |
| Towards automated deep learning: Analysis of the autodl challenge series 2019 Z Liu, Z Xu, S Rajaa, M Madadi, JCSJ Junior, S Escalera, A Pavao, ... NeurIPS 2019 Competition and Demonstration Track, 242-252, 2020 | 26 | 2020 |
| COVID-19 asymptomatic infection estimation Y Yu, YR Liu, FM Luo, WW Tu, DC Zhan, G Yu, ZH Zhou medRxiv, 2020.04. 19.20068072, 2020 | 26 | 2020 |
Quanming YaoAssistant Professor, EE Department, Tsinghua UniversityVahvistettu sähköpostiosoite verkkotunnuksessa tsinghua.edu.cn
