| Max-value entropy search for efficient Bayesian optimization Z Wang, S Jegelka International Conference on Machine Learning, 3627-3635, 2017 | 446 | 2017 |
| Batched large-scale Bayesian optimization in high-dimensional spaces Z Wang, C Gehring, P Kohli, S Jegelka International Conference on Artificial Intelligence and Statistics, 745-754, 2018 | 199 | 2018 |
| Batched high-dimensional Bayesian optimization via structural kernel learning Z Wang, C Li, S Jegelka, P Kohli International Conference on Machine Learning, 3656-3664, 2017 | 129 | 2017 |
| Learning to guide task and motion planning using score-space representation B Kim, Z Wang, LP Kaelbling, T Lozano-Pérez The International Journal of Robotics Research 38 (7), 793-812, 2019 | 100 | 2019 |
| Learning compositional models of robot skills for task and motion planning Z Wang, CR Garrett, LP Kaelbling, T Lozano-Pérez The International Journal of Robotics Research 40 (6-7), 866-894, 2021 | 93 | 2021 |
| Scalable inference for logistic-normal topic models J Chen, J Zhu, Z Wang, X Zheng, B Zhang Advances in neural information processing systems 26, 2013 | 93 | 2013 |
| Optimization as estimation with Gaussian processes in bandit settings Z Wang, B Zhou, S Jegelka Artificial Intelligence and Statistics, 1022-1031, 2016 | 88 | 2016 |
| Plex: Towards reliability using pretrained large model extensions D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... arXiv preprint arXiv:2207.07411, 2022 | 81 | 2022 |
| Active model learning and diverse action sampling for task and motion planning Z Wang, CR Garrett, LP Kaelbling, T Lozano-Pérez 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 71 | 2018 |
| Discriminative non-negative matrix factorization for single-channel speech separation Z Wang, F Sha 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 59 | 2014 |
| Regret bounds for meta bayesian optimization with an unknown gaussian process prior Z Wang, B Kim, LP Kaelbling Advances in Neural Information Processing Systems 31, 2018 | 41 | 2018 |
| Towards learning universal hyperparameter optimizers with transformers Y Chen, X Song, C Lee, Z Wang, R Zhang, D Dohan, K Kawakami, ... Advances in Neural Information Processing Systems 35, 32053-32068, 2022 | 31 | 2022 |
| Learning sparse relational transition models V Xia, Z Wang, LP Kaelbling arXiv preprint arXiv:1810.11177, 2018 | 29 | 2018 |
| Grammar prompting for domain-specific language generation with large language models B Wang, Z Wang, X Wang, Y Cao, R A Saurous, Y Kim Advances in Neural Information Processing Systems 36, 2024 | 22 | 2024 |
| Pre-trained Gaussian processes for Bayesian optimization Z Wang, GE Dahl, K Swersky, C Lee, Z Nado, J Gilmer, J Snoek, ... arXiv preprint arXiv:2109.08215, 2021 | 21 | 2021 |
| Focused model-learning and planning for non-Gaussian continuous state-action systems Z Wang, S Jegelka, LP Kaelbling, T Lozano-Pérez 2017 IEEE International conference on robotics and automation (ICRA), 3754-3761, 2017 | 20 | 2017 |
| Plex: towards reliability using pretrained large model extensions (2022) D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... URL https://arxiv. org/abs/2207.07411, 0 | 8 | |
| HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes Z Fan, X Han, Z Wang arXiv preprint arXiv:2212.10538, 2022 | 4 | 2022 |
| Pre-training helps bayesian optimization too Z Wang, GE Dahl, K Swersky, C Lee, Z Mariet, Z Nado, J Gilmer, J Snoek, ... arXiv preprint arXiv:2207.03084, 2022 | 4 | 2022 |
| Gaussian process probes (gpp) for uncertainty-aware probing Z Wang, A Ku, J Baldridge, T Griffiths, B Kim Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
Stefanie JegelkaTUM and MITVerified email at mit.edu
