| Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems 25, 2012 | 13200 | 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 | 7527 | 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 28, 2015 | 5036 | 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 | 4476 | 2018 |
| 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 | 1516 | 2015 |
| Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015 | 1353 | 2015 |
| Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007 | 1299 | 2007 |
| Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015 | 1267 | 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 | 1174 | 2016 |
| Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems 26, 2013 | 1077 | 2013 |
| Bayesian reaction optimization as a tool for chemical synthesis BJ Shields, J Stevens, J Li, M Parasram, F Damani, JIM Alvarado, ... Nature 590 (7844), 89-96, 2021 | 1043 | 2021 |
| Gaussian process kernels for pattern discovery and extrapolation A Wilson, R Adams International conference on machine learning, 1067-1075, 2013 | 946 | 2013 |
| Mapping sub-second structure in mouse behavior AB Wiltschko, MJ Johnson, G Iurilli, RE Peterson, JM Katon, ... Neuron 88 (6), 1121-1135, 2015 | 878 | 2015 |
| Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014 | 840 | 2014 |
| Elliptical slice sampling I Murray, R Adams, D MacKay Proceedings of the thirteenth international conference on artificial …, 2010 | 632 | 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 29, 2016 | 614 | 2016 |
| Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems 28, 2015 | 501 | 2015 |
| Autograd: Effortless gradients in numpy D Maclaurin, D Duvenaud, RP Adams ICML 2015 AutoML workshop 238 (5), 2015 | 383 | 2015 |
| Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014 | 365 | 2014 |
| Discovering latent network structure in point process data S Linderman, R Adams International conference on machine learning, 1413-1421, 2014 | 364 | 2014 |
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
