| Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017 | 3087 | 2017 |
| Mmd gan: Towards deeper understanding of moment matching network CL Li, WC Chang, Y Cheng, Y Yang, B Póczos Advances in neural information processing systems 30, 2017 | 883 | 2017 |
| Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh arXiv preprint arXiv:1810.02054, 2018 | 854 | 2018 |
| Bayesian optimization with robust bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in Neural Information Processing Systems, 4134-4142, 2016 | 820* | 2016 |
| Neural architecture search with bayesian optimisation and optimal transport K Kandasamy, W Neiswanger, J Schneider, B Poczos, EP Xing Advances in neural information processing systems 31, 2018 | 740 | 2018 |
| Stochastic variance reduction for nonconvex optimization SJ Reddi, A Hefny, S Sra, B Poczos, A Smola International conference on machine learning, 314-323, 2016 | 700 | 2016 |
| Characterizing and avoiding negative transfer Z Wang, Z Dai, B Póczos, J Carbonell Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 608 | 2019 |
| Found in translation: Learning robust joint representations by cyclic translations between modalities H Pham, PP Liang, T Manzini, LP Morency, B Póczos Proceedings of the AAAI conference on artificial intelligence 33 (01), 6892-6899, 2019 | 455 | 2019 |
| High dimensional Bayesian optimisation and bandits via additive models K Kandasamy, J Schneider, B Póczos International conference on machine learning, 295-304, 2015 | 445 | 2015 |
| One network to solve them all--solving linear inverse problems using deep projection models JH Rick Chang, CL Li, B Poczos, BVK Vijaya Kumar, ... Proceedings of the IEEE International Conference on Computer Vision, 5888-5897, 2017 | 408 | 2017 |
| Competence-based curriculum learning for neural machine translation EA Platanios, O Stretcu, G Neubig, B Poczos, TM Mitchell arXiv preprint arXiv:1903.09848, 2019 | 387 | 2019 |
| Graph neural tangent kernel: Fusing graph neural networks with graph kernels SS Du, K Hou, RR Salakhutdinov, B Poczos, R Wang, K Xu Advances in neural information processing systems 32, 2019 | 313 | 2019 |
| Gradient descent can take exponential time to escape saddle points SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos Advances in neural information processing systems 30, 2017 | 313 | 2017 |
| Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International conference on artificial intelligence and statistics, 133-142, 2018 | 308 | 2018 |
| Equivariance through parameter-sharing S Ravanbakhsh, J Schneider, B Poczos International conference on machine learning, 2892-2901, 2017 | 285 | 2017 |
| Multi-fidelity bayesian optimisation with continuous approximations K Kandasamy, G Dasarathy, J Schneider, B Póczos International conference on machine learning, 1799-1808, 2017 | 268 | 2017 |
| Learning to predict the cosmological structure formation S He, Y Li, Y Feng, S Ho, S Ravanbakhsh, W Chen, B Póczos Proceedings of the National Academy of Sciences 116 (28), 13825-13832, 2019 | 264 | 2019 |
| Point cloud gan CL Li, M Zaheer, Y Zhang, B Poczos, R Salakhutdinov arXiv preprint arXiv:1810.05795, 2018 | 262 | 2018 |
| Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima S Du, J Lee, Y Tian, A Singh, B Poczos International Conference on Machine Learning, 1339-1348, 2018 | 253 | 2018 |
| Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly K Kandasamy, KR Vysyaraju, W Neiswanger, B Paria, CR Collins, ... Journal of Machine Learning Research 21 (81), 1-27, 2020 | 242 | 2020 |
Jeff SchneiderCarnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证