| On the importance of hyperparameter optimization for model-based reinforcement learning B Zhang, R Rajan, L Pineda, N Lambert, A Biedenkapp, K Chua, F Hutter, ... International Conference on Artificial Intelligence and Statistics, 4015-4023, 2021 | 106 | 2021 |
| Active MR k-space Sampling with Reinforcement Learning L Pineda, S Basu, A Romero, R Calandra, M Drozdzal Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 77 | 2020 |
| Theseus: A library for differentiable nonlinear optimization L Pineda, T Fan, M Monge, S Venkataraman, P Sodhi, RTQ Chen, J Ortiz, ... Advances in Neural Information Processing Systems 35, 3801-3818, 2022 | 74 | 2022 |
| Off-belief learning H Hu, A Lerer, B Cui, L Pineda, N Brown, J Foerster International Conference on Machine Learning, 4369-4379, 2021 | 58 | 2021 |
| Learning causal state representations of partially observable environments A Zhang, ZC Lipton, L Pineda, K Azizzadenesheli, A Anandkumar, L Itti, ... arXiv preprint arXiv:1906.10437, 2019 | 53 | 2019 |
| Mbrl-lib: A modular library for model-based reinforcement learning L Pineda, B Amos, A Zhang, NO Lambert, R Calandra arXiv preprint arXiv:2104.10159, 2021 | 45 | 2021 |
| Hierarchical approach to transfer of control in semi-autonomous systems KH Wray, L Pineda, S Zilberstein Proceedings of the 2016 International Conference on Autonomous Agents …, 2016 | 43 | 2016 |
| On the evaluation of conditional GANs T DeVries, A Romero, L Pineda, GW Taylor, M Drozdzal arXiv preprint arXiv:1907.08175, 2019 | 42 | 2019 |
| Equi-reward utility maximizing design in stochastic environments S Keren, L Pineda, A Gal, E Karpas, S Zilberstein HSDIP 2017, 19, 2017 | 42 | 2017 |
| Planning Under Uncertainty Using Reduced Models: Revisiting Determinization. LE Pineda, S Zilberstein Proceedings of the Twenty-Fourth International Conference on Automated …, 2014 | 39 | 2014 |
| K-level reasoning for zero-shot coordination in hanabi B Cui, H Hu, L Pineda, J Foerster Advances in Neural Information Processing Systems 34, 8215-8228, 2021 | 30 | 2021 |
| Active 3D shape reconstruction from vision and touch E Smith, D Meger, L Pineda, R Calandra, J Malik, A Romero Soriano, ... Advances in Neural Information Processing Systems 34, 16064-16078, 2021 | 26 | 2021 |
| Fault-Tolerant Planning under Uncertainty L Pineda, Y Lu, S Zilberstein, CV Goldman International Joint Conference on Artificial Intelligence, 2013 | 25 | 2013 |
| Continual planning for search and rescue robots L Pineda, T Takahashi, HT Jung, S Zilberstein, R Grupen 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids …, 2015 | 23 | 2015 |
| Fast SSP Solvers Using Short-Sighted Labeling L Pineda, KH Wray, S Zilberstein Thirty-First AAAI Conference on Artificial Intelligence, 2017, 2017 | 17 | 2017 |
| On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction T Bakker, M Muckley, A Romero-Soriano, M Drozdzal, L Pineda International Conference on Medical Imaging with Deep Learning, 63-85, 2022 | 16 | 2022 |
| Elucidating image-to-set prediction: An analysis of models, losses and datasets L Pineda, A Salvador, M Drozdzal, A Romero arXiv preprint arXiv:1904.05709, 2019 | 14 | 2019 |
| Efficient heuristic search for optimal environment redesign S Keren, L Pineda, A Gal, E Karpas, S Zilberstein Proceedings of the International Conference on Automated Planning and …, 2019 | 12 | 2019 |
| Revisiting multi-objective MDPs with relaxed lexicographic preferences LE Pineda, KH Wray, S Zilberstein 2015 AAAI Fall Symposium Series, 2015 | 12 | 2015 |
| Planning in stochastic environments with goal uncertainty S Saisubramanian, KH Wray, L Pineda, S Zilberstein 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 7 | 2019 |
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
