| Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems, 2951-2959, 2012 | 3833 | 2012 |
| Taking the human out of the loop: A review of Bayesian optimization B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas Proceedings of the IEEE 104 (1), 148-175, 2015 | 1485 | 2015 |
| Convolutional networks on graphs for learning molecular fingerprints DK Duvenaud, D Maclaurin, J Iparraguirre, R Bombarell, T Hirzel, ... Advances in neural information processing systems, 2224-2232, 2015 | 1453 | 2015 |
| Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276, 2018 | 882 | 2018 |
| Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007 | 525 | 2007 |
| Scalable bayesian optimization using deep neural networks J Snoek, O Rippel, K Swersky, R Kiros, N Satish, N Sundaram, M Patwary, ... International conference on machine learning, 2171-2180, 2015 | 487 | 2015 |
| Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International Conference on Machine Learning, 1861-1869, 2015 | 465 | 2015 |
| Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach R Gómez-Bombarelli, J Aguilera-Iparraguirre, TD Hirzel, D Duvenaud, ... Nature materials 15 (10), 1120-1127, 2016 | 400 | 2016 |
| Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International Conference on Machine Learning, 2113-2122, 2015 | 396 | 2015 |
| Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems, 2004-2012, 2013 | 394 | 2013 |
| Gaussian process kernels for pattern discovery and extrapolation A Wilson, R Adams International conference on machine learning, 1067-1075, 2013 | 381 | 2013 |
| Elliptical slice sampling I Murray, R Adams, D MacKay Proceedings of the thirteenth international conference on artificial …, 2010 | 356 | 2010 |
| Composing graphical models with neural networks for structured representations and fast inference MJ Johnson, DK Duvenaud, A Wiltschko, RP Adams, SR Datta Advances in neural information processing systems, 2946-2954, 2016 | 306 | 2016 |
| Mapping sub-second structure in mouse behavior AB Wiltschko, MJ Johnson, G Iurilli, RE Peterson, JM Katon, ... Neuron 88 (6), 1121-1135, 2015 | 264 | 2015 |
| Discovering latent network structure in point process data S Linderman, R Adams International Conference on Machine Learning, 1413-1421, 2014 | 209 | 2014 |
| Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems, 1732-1740, 2010 | 206 | 2010 |
| Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities RP Adams, I Murray, DJC MacKay Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 196 | 2009 |
| Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014 | 194 | 2014 |
| Quantitative diagnosis of malignant pleural effusions by single-cell mechanophenotyping TK Henry, DR Gossett, YS Moon, M Masaeli, M Sohsman, Y Ying, ... Science translational medicine 5 (212), 212ra163-212ra163, 2013 | 188 | 2013 |
| Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems, 2449-2457, 2015 | 181 | 2015 |
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
