| Max-value Entropy Search for Efficient Bayesian Optimization Z Wang, S Jegelka International Conference on Machine Learning (ICML), 2017 | 363 | 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 (AISTATS), 2018 | 153 | 2018 |
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning Z Wang, C Li, S Jegelka, P Kohli International Conference on Machine Learning (ICML), 2017 | 110 | 2017 |
| Scalable Inference for Logistic-Normal Topic Models J Chen, J Zhu, Z Wang, X Zheng, B Zhang Advances in Neural Information Processing Systems (NeurIPS), 2445-2453, 2013 | 88 | 2013 |
| Optimization as Estimation with Gaussian Processes in Bandit Settings Z Wang, B Zhou, S Jegelka International Conference on Artificial Intelligence and Statistics (AISTATS), 2016 | 85 | 2016 |
| Learning to guide task and motion planning using score-space representation B Kim, Z Wang, LP Kaelbling, T Lozano-Perez International Journal of Robotics Research (IJRR), 2019 | 80 | 2019 |
| 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 | 61 | 2018 |
| 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 (IJRR) 40 (6-7), 866-894, 2021 | 58 | 2021 |
| Discriminative Non-negative Matrix Factorization for Single-Channel Speech Separation Z Wang, F Sha Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014 | 55 | 2014 |
| 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 | 35 | 2022 |
| Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior Z Wang, B Kim, LP Kaelbling Advances in Neural Information Processing Systems (NeurIPS), 10477-10488, 2018 | 35 | 2018 |
| Learning sparse relational transition models V Xia, Z Wang, K Allen, T Silver, LP Kaelbling International Conference on Learning Representations (ICLR), 2019 | 23 | 2019 |
| Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems Z Wang, S Jegelka, LP Kaelbling, T Lozano-Pérez IEEE Conference on Robotics and Automation (ICRA), 2017 | 16 | 2017 |
| 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, 2023 | 8 | 2023 |
| 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 | 8 | 2022 |
| 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 | 5 | |
| Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers Z Wang, GE Dahl, K Swersky, C Lee, ZE Mariet, Z Nado, J Gilmer, ... | 3 | 2021 |
| Pre-training helps Bayesian optimization too Z Wang, GE Dahl, K Swersky, C Lee, Z Mariet, Z Nado, J Gilmer, J Snoek, ... ICML2022 Workshop on Adaptive Experimental Design and Active Learning in the …, 2022 | 2 | 2022 |
| Deep Uncertainty and the Search for Proteins Z Mariet, G Jerfel, Z Wang, C Angermüller, D Belanger, S Vora, M Bileschi, ... Workshop: Machine Learning for Molecules, 2020 | 2 | 2020 |
| Optimization as Estimation with Gaussian Processes in Bandit Settings Z Wang, B Zhou, S Jegelka arXiv preprint arXiv:1510.06423, 2015 | 1 | 2015 |
Stefanie JegelkaMITVerified email at mit.edu
