José Miguel Hernández-Lobato
José Miguel Hernández-Lobato
Associate Professor in Machine Learning, University of Cambridge
Verified email at - Homepage
Cited by
Cited by
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
Probabilistic backpropagation for scalable learning of bayesian neural networks
JM Hernández-Lobato, R Adams
International conference on machine learning, 1861-1869, 2015
Grammar variational autoencoder
MJ Kusner, B Paige, JM Hernández-Lobato
International Conference on Machine Learning, 1945-1954, 2017
Minerva: Enabling low-power, highly-accurate deep neural network accelerators
B Reagen, P Whatmough, R Adolf, S Rama, H Lee, SK Lee, ...
2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture …, 2016
Predictive entropy search for efficient global optimization of black-box functions
JM Hernández-Lobato, MW Hoffman, Z Ghahramani
arXiv preprint arXiv:1406.2541, 2014
Gans for sequences of discrete elements with the gumbel-softmax distribution
MJ Kusner, JM Hernández-Lobato
arXiv preprint arXiv:1611.04051, 2016
Deep Gaussian processes for regression using approximate expectation propagation
T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner
International conference on machine learning, 1472-1481, 2016
Black-box alpha divergence minimization
J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ...
International Conference on Machine Learning, 1511-1520, 2016
Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning
S Depeweg, JM Hernandez-Lobato, F Doshi-Velez, S Udluft
International Conference on Machine Learning, 1184-1193, 2018
Predictive entropy search for multi-objective bayesian optimization
D Hernández-Lobato, J Hernandez-Lobato, A Shah, R Adams
International Conference on Machine Learning, 1492-1501, 2016
Learning and policy search in stochastic dynamical systems with bayesian neural networks
S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft
arXiv preprint arXiv:1605.07127, 2016
Collaborative gaussian processes for preference learning
N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato
Advances in neural information processing systems 25, 2096-2104, 2012
Stochastic expectation propagation
Y Li, JM Hernández-Lobato, RE Turner
arXiv preprint arXiv:1506.04132, 2015
Probabilistic matrix factorization with non-random missing data
JM Hernández-Lobato, N Houlsby, Z Ghahramani
International Conference on Machine Learning, 1512-1520, 2014
Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space
JM Hernández-Lobato, J Requeima, EO Pyzer-Knapp, A Aspuru-Guzik
International conference on machine learning, 1470-1479, 2017
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck
International Conference on Machine Learning, 1645-1654, 2017
Predictive entropy search for bayesian optimization with unknown constraints
JM Hernández-Lobato, M Gelbart, M Hoffman, R Adams, Z Ghahramani
International conference on machine learning, 1699-1707, 2015
A general framework for constrained bayesian optimization using information-based search
JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ...
MIT Press, 2016
Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation.
D Hernández-Lobato, JM Hernández-Lobato, P Dupont
Journal of Machine Learning Research 14 (7), 2013
Deterministic variational inference for robust bayesian neural networks
A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt
arXiv preprint arXiv:1810.03958, 2018
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