| Max-value Entropy Search for Efficient Bayesian Optimization Z Wang, S Jegelka International Conference on Machine Learning (ICML), 2017 | 264 | 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 | 109 | 2018 |
| 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 |
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning Z Wang, C Li, S Jegelka, P Kohli International Conference on Machine Learning (ICML), 2017 | 87 | 2017 |
| Optimization as Estimation with Gaussian Processes in Bandit Settings Z Wang, B Zhou, S Jegelka International Conference on Artificial Intelligence and Statistics (AISTATS), 2016 | 78 | 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 | 57 | 2019 |
| Discriminative Non-negative Matrix Factorization for Single-Channel Speech Separation Z Wang, F Sha Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014 | 53 | 2014 |
| 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 | 45 | 2018 |
| 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 | 23 | 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 | 20 | 2021 |
| Learning sparse relational transition models V Xia, Z Wang, K Allen, T Silver, LP Kaelbling International Conference on Learning Representations (ICLR), 2019 | 19 | 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 | 12 | 2017 |
| 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:2109.08215, 2022 | 1 | 2022 |
| Optimization as Estimation with Gaussian Processes in Bandit Settings Z Wang, B Zhou, S Jegelka arXiv preprint arXiv:1510.06423, 2015 | 1 | 2015 |
| Fast Learning with Noise in Deep Neural Nets Z Lu, Z Wang, F Sha NIPS Workshop: Perturbations, Optimization, and Statistics, 2014 | 1 | 2014 |
| Towards Learning Universal Hyperparameter Optimizers with Transformers Y Chen, X Song, C Lee, Z Wang, Q Zhang, D Dohan, K Kawakami, ... arXiv preprint arXiv:2205.13320, 2022 | | 2022 |
| Robot learning with strong priors Z Wang Massachusetts Institute of Technology, 2020 | | 2020 |
| Motion Planning using Naturally Annoying Grammars (NAGs) CR Garrett, C Gehring, G Goretkin, Z Mariet, Z Wang SIGTBD, 2016 | | 2016 |
| Learning structured transition models for multi-object manipulation V Xia, Z Wang, LP Kaelbling | | |
Stefanie JegelkaMITVerified email at mit.edu
