| Gaussian process bandit optimisation with multi-fidelity evaluations K Kandasamy, G Dasarathy, JB Oliva, J Schneider, B Póczos Advances in neural information processing systems 29, 2016 | 152 | 2016 |
| MolecularRNN: Generating realistic molecular graphs with optimized properties M Popova, M Shvets, J Oliva, O Isayev arXiv preprint arXiv:1905.13372, 2019 | 110 | 2019 |
| Meta-curvature E Park, JB Oliva Advances in Neural Information Processing Systems 32, 2019 | 106 | 2019 |
| Transformation autoregressive networks J Oliva, A Dubey, M Zaheer, B Poczos, R Salakhutdinov, E Xing, ... International Conference on Machine Learning, 3898-3907, 2018 | 84 | 2018 |
| Distribution to distribution regression J Oliva, B Póczos, J Schneider International Conference on Machine Learning, 1049-1057, 2013 | 83 | 2013 |
| Estimating cosmological parameters from the dark matter distribution S Ravanbakhsh, J Oliva, S Fromenteau, L Price, S Ho, J Schneider, ... International Conference on Machine Learning, 2407-2416, 2016 | 78 | 2016 |
| Bayesian nonparametric kernel-learning JB Oliva, A Dubey, AG Wilson, B Póczos, J Schneider, EP Xing Artificial intelligence and statistics, 1078-1086, 2016 | 75 | 2016 |
| Multi-fidelity gaussian process bandit optimisation K Kandasamy, G Dasarathy, J Oliva, J Schneider, B Poczos Journal of Artificial Intelligence Research 66, 151-196, 2019 | 74 | 2019 |
| The statistical recurrent unit JB Oliva, B Póczos, J Schneider International Conference on Machine Learning, 2671-2680, 2017 | 51 | 2017 |
| Fast distribution to real regression J Oliva, W Neiswanger, B Póczos, J Schneider, E Xing Artificial Intelligence and Statistics, 706-714, 2014 | 45 | 2014 |
| Spatio-temporal matching for human pose estimation in video F Zhou, F De la Torre IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (8), 1492-1504, 2016 | 31 | 2016 |
| Fast function to function regression J Oliva, W Neiswanger, B Póczos, E Xing, H Trac, S Ho, J Schneider Artificial Intelligence and Statistics, 717-725, 2015 | 30 | 2015 |
| ACFlow: Flow models for arbitrary conditional likelihoods Y Li, S Akbar, J Oliva International Conference on Machine Learning, 5831-5841, 2020 | 27* | 2020 |
| Exchangeable neural ode for set modeling Y Li, H Yi, C Bender, S Shan, JB Oliva Advances in Neural Information Processing Systems 33, 6936-6946, 2020 | 19 | 2020 |
| Active feature acquisition with generative surrogate models Y Li, J Oliva International Conference on Machine Learning, 6450-6459, 2021 | 18 | 2021 |
| Multiscale score matching for out-of-distribution detection A Mahmood, J Oliva, M Styner arXiv preprint arXiv:2010.13132, 2020 | 18 | 2020 |
| Linear-time learning on distributions with approximate kernel embeddings D Sutherland, J Oliva, B Póczos, J Schneider Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 18 | 2016 |
| Exchangeable generative models with flow scans C Bender, K O'Connor, Y Li, J Garcia, J Oliva, M Zaheer Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10053 …, 2020 | 12* | 2020 |
| Fusso: Functional shrinkage and selection operator JB Oliva, B Póczos, T Verstynen, A Singh, J Schneider, FC Yeh, ... Artificial Intelligence and Statistics (AISTATS) 33, 2014 | 12 | 2014 |
| Arbitrary conditional distributions with energy R Strauss, JB Oliva Advances in Neural Information Processing Systems 34, 752-763, 2021 | 10 | 2021 |
