| 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 | 150 | 2019 |
| Metamath: Bootstrap your own mathematical questions for large language models L Yu, W Jiang, H Shi, J Yu, Z Liu, Y Zhang, JT Kwok, Z Li, A Weller, W Liu arXiv preprint arXiv:2309.12284, 2023 | 147 | 2023 |
| Progressive-hint prompting improves reasoning in large language models C Zheng, Z Liu, E Xie, Z Li, Y Li arXiv preprint arXiv:2304.09797, 2023 | 96 | 2023 |
| Bisakha Ray, Mehreen Saeed, Michele Sebag, Alexander Statnikov, Wei-Wei Tu, and Evelyne Viegas. Analysis of the AutoML Challenge Series 2015–2018 I Guyon, L Sun-Hosoya, M Boullé, HJ Escalante, S Escalera, Z Liu, ... AutoML, Springer series on Challenges in Machine Learning, 2019 | 32 | 2019 |
| Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 Z Liu, A Pavao, Z Xu, S Escalera, F Ferreira, I Guyon, S Hong, F Hutter, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (9), 3108-3125, 2021 | 30 | 2021 |
| AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data R Egele, P Balaprakash, I Guyon, V Vishwanath, F Xia, R Stevens, Z Liu Proceedings of the International Conference for High Performance Computing …, 2021 | 26 | 2021 |
| 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 | 24 | 2020 |
| Deep statistical solvers B Donon, Z Liu, W Liu, I Guyon, A Marot, M Schoenauer Advances in Neural Information Processing Systems 33, 7910-7921, 2020 | 22 | 2020 |
| Dt-solver: Automated theorem proving with dynamic-tree sampling guided by proof-level value function H Wang, Y Yuan, Z Liu, J Shen, Y Yin, J Xiong, E Xie, H Shi, Y Li, L Li, ... Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023 | 17 | 2023 |
| Leap nets for system identification and application to power systems B Donon, B Donnot, I Guyon, Z Liu, A Marot, P Panciatici, M Schoenauer Neurocomputing 416, 316-327, 2020 | 16 | 2020 |
| Autocv challenge design and baseline results Z Liu, I Guyon, JJ Junior, M Madadi, S Escalera, A Pavao, HJ Escalante, ... CAp 2019-Conférence sur l'Apprentissage Automatique, 2019 | 16 | 2019 |
| A survey of reasoning with foundation models J Sun, C Zheng, E Xie, Z Liu, R Chu, J Qiu, J Xu, M Ding, H Li, M Geng, ... arXiv preprint arXiv:2312.11562, 2023 | 15 | 2023 |
| Fimo: A challenge formal dataset for automated theorem proving C Liu, J Shen, H Xin, Z Liu, Y Yuan, H Wang, W Ju, C Zheng, Y Yin, L Li, ... arXiv preprint arXiv:2309.04295, 2023 | 15 | 2023 |
| Gaining wisdom from setbacks: Aligning large language models via mistake analysis K Chen, C Wang, K Yang, J Han, L Hong, F Mi, H Xu, Z Liu, W Huang, Z Li, ... arXiv preprint arXiv:2310.10477, 2023 | 14 | 2023 |
| Bisakha Ray, Mehreen Saeed, Michèle Sebag, et al I Guyon, L Sun-Hosoya, M Boullé, HJ Escalante, S Escalera, Z Liu, ... Analysis of the automl challenge series. Automated Machine Learning 177, 2019 | 14 | 2019 |
| Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification A El Baz, I Ullah, E Alcobaça, AC Carvalho, H Chen, F Ferreira, H Gouk, ... NeurIPS 2021 Competitions and Demonstrations Track, 80-96, 2022 | 11 | 2022 |
| LEAP nets for power grid perturbations B Donnot, B Donon, I Guyon, Z Liu, A Marot, P Panciatici, M Schoenauer arXiv preprint arXiv:1908.08314, 2019 | 11 | 2019 |
| Lego-prover: Neural theorem proving with growing libraries H Xin, H Wang, C Zheng, L Li, Z Liu, Q Cao, Y Huang, J Xiong, H Shi, ... arXiv preprint arXiv:2310.00656, 2023 | 9 | 2023 |
| Towards automated computer vision: analysis of the AutoCV challenges 2019 Z Liu, Z Xu, S Escalera, I Guyon, JCSJ Júnior, M Madadi, A Pavao, ... Pattern Recognition Letters 135, 196-203, 2020 | 9 | 2020 |
| Mustard: Mastering uniform synthesis of theorem and proof data Y Huang, X Lin, Z Liu, Q Cao, H Xin, H Wang, Z Li, L Song, X Liang arXiv preprint arXiv:2402.08957, 2024 | 8 | 2024 |
Sergio EscaleraProf., ICREA Academy, University of Barcelona, Computer Vision Center, ELLIS & IAPR & AAIA Fellow在 ub.edu 的电子邮件经过验证
