Department of Engineering

Dr Miguel Hernandez Lobato - Publications

Number of items: 73.

Article

Bhardwaj, K and Havasi, M and Yao, Y and Brooks, DM and Lobato, JMH and Wei, GY (2019) Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization. IEEE Computer Architecture Letters, 18. pp. 119-123. ISSN 1556-6056

Wu, A and Nowozin, S and Meeds, E and Turner, RE and Hernández-Lobato, JM and Gaunt, AL (2019) Deterministic variational inference for robust Bayesian neural networks. 7th International Conference on Learning Representations, ICLR 2019.

Depeweg, S and Hernández-Lobato, JM and Doshi-Velez, F and Udluft, S (2019) Learning and policy search in stochastic dynamical systems with Bayesian neural networks. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.

Gong, W and Li, Y and Hernández-Lobato, JM (2019) Meta-learning for stochastic gradient MCMC. 7th International Conference on Learning Representations, ICLR 2019.

Havasi, M and Peharz, R and Hernández-Lobato, JM (2019) Minimal random code learning: Getting bits back from compressed model parameters. 7th International Conference on Learning Representations, ICLR 2019.

Bradshaw, J and Kusner, MJ and Paige, B and Segler, MHS and Hernández-Lobato, JM (2019) A generative model for electron paths. 7th International Conference on Learning Representations, ICLR 2019.

Gómez-Bombarelli, R and Wei, JN and Duvenaud, D and Hernández-Lobato, JM and Sánchez-Lengeling, B and Sheberla, D and Aguilera-Iparraguirre, J and Hirzel, TD and Adams, RP and Aspuru-Guzik, A (2018) Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science, 4. pp. 268-276. ISSN 2374-7943

Depeweg, S and Hernandez-Lobato, JM and Doshi-Velez, F and Udluft, S (2018) Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. 35th International Conference on Machine Learning, ICML 2018, 3. pp. 1920-1934.

Havasi, M and Hernández-Lobato, JM and Murillo-Fuentes, JJ (2018) Inference in deep Gaussian processes using stochastic gradient hamiltonian Monte Carlo. Advances in Neural Information Processing Systems, 2018-D. pp. 7506-7516. ISSN 1049-5258

Janz, D and Van Der Westhuizen, J and Paige, B and Kusner, MJ and Hernández-Lobato, JM (2018) Learning a generative model for validity in complex discrete structures. 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.

Depeweg, S and Hernández-Lobato, JM and Udluft, S and Runkler, T (2018) Sensitivity analysis for predictive uncertainty in Bayesian neural networks. ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 279-284.

Hernández-Lobato, JM and Requeima, J and Pyzer-Knapp, EO and Aspuru-Guzik, A (2017) Parallel and distributed thompson sampling for large-scale accelerated exploration of chemical space. 34th International Conference on Machine Learning, ICML 2017, 3. pp. 2325-2334.

Hernández-Lobato, JM and Gelbart, MA and Adams, RP and Hoffman, MW and Ghahramani, Z (2016) A General Framework for Constrained Bayesian Optimization using Information-based Search. Journal of Machine Learning Research, 17.

Hernández-Lobato, JM and Li, Y and Rowland, M and Hernández-Lobato, D and Bui, TD and Turner, RE (2016) Black-Box α-divergence minimization. Proceedings of the 33rd International Conference on Machine Learning, 48. pp. 1511-1520.

Hernández-Lobato, D and Hernández-Lobato, JM and Shah, A and Adams, RP (2016) Predictive entropy search for multi-objective Bayesian optimization. 33rd International Conference on Machine Learning, ICML 2016, 3. pp. 2219-2237.

Hernández-Lobato, D and Hernández-Lobato, JM (2016) Scalable gaussian process classification via expectation propagation. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. pp. 168-176.

Hernández-Lobato, JM and Hernández-Lobato, D and Suárez, A (2015) Expectation propagation in linear regression models with spike-and-slab priors. Machine Learning, 99. pp. 437-487. ISSN 0885-6125

Hernández-Lobato, JM and Gelbart, MA and Hoffman, MW and Adams, RP and Ghahramani, Z (2015) Predictive Entropy Search for Bayesian optimization with unknown constraints. 32nd International Conference on Machine Learning, ICML 2015, 2. pp. 1699-1707.

Hernández-Lobato, JM and Hoffman, MW and Ghahramani, Z (2014) Predictive entropy search for efficient global optimization of black-box functions. Advances in Neural Information Processing Systems, 1. pp. 918-926. ISSN 1049-5258

Hernandez-Lobato, D and Miguel Hernandez-Lobato, J and Dupont, P (2013) Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation. JOURNAL OF MACHINE LEARNING RESEARCH, 14. pp. 1891-1945. ISSN 1532-4435

Houlsby, N and Hernández-Lobato, JM and Huszár, F and Ghahramani, Z (2012) Collaborative Gaussian processes for preference learning. Advances in Neural Information Processing Systems, 3. pp. 2096-2104. ISSN 1049-5258

Hernandez-Lobato, JM and Suarez, A (2011) Semiparametric bivariate Archimedean copulas. COMPUT STAT DATA AN, 55. pp. 2038-2058. ISSN 0167-9473

Hernandez-Lobato, JM and Hernandez-Lobato, D and Suarez, A (2011) Network-based sparse Bayesian classification. PATTERN RECOGN, 44. pp. 886-900. ISSN 0031-3203

Hernandez-Lobato, D and Hernandez-Lobato, JM and Suarez, A (2010) Expectation Propagation for microarray data classification. PATTERN RECOGN LETT, 31. pp. 1618-1626. ISSN 0167-8655

Hernandez-Lobato, D and Hernandez-Lobato, JM (2008) Bayes Machines for binary classification. PATTERN RECOGN LETT, 29. pp. 1466-1473. ISSN 0167-8655

Pinsler, R and Gordon, J and Nalisnick, E and Hernández-Lobato, JM Bayesian Batch Active Learning as Sparse Subset Approximation. (Unpublished)

Gordon, J and Hernández-Lobato, JM Bayesian Semisupervised Learning with Deep Generative Models. (Unpublished)

Mahmood, O and Hernández-Lobato, JM A COLD Approach to Generating Optimal Samples. (Unpublished)

Griffiths, R-R and Hernández-Lobato, JM Constrained Bayesian Optimization for Automatic Chemical Design. (Unpublished)

Hernández-Lobato, JM and Hernández-Lobato, D Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors. (Unpublished)

Lu, C and Schölkopf, B and Hernández-Lobato, JM Deconfounding Reinforcement Learning in Observational Settings. (Unpublished)

Havasi, M and Hernández-Lobato, JM and Murillo-Fuentes, JJ Deep Gaussian Processes with Decoupled Inducing Inputs. (Unpublished)

Nalisnick, E and Hernández-Lobato, JM and Smyth, P Dropout as a Structured Shrinkage Prior. (Unpublished)

Wu, Y and Hernández-Lobato, JM and Ghahramani, Z Dynamic Covariance Models for Multivariate Financial Time Series. (Unpublished)

Ma, C and Tschiatschek, S and Palla, K and Hernández-Lobato, JM and Nowozin, S and Zhang, C EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. (Unpublished)

Zhang, Y and Hernández-Lobato, JM Ergodic Inference: Accelerate Convergence by Optimisation. (Unpublished)

Kusner, MJ and Hernández-Lobato, JM GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. (Unpublished)

Lopez-Paz, D and Hernández-Lobato, JM and Ghahramani, Z Gaussian Process Vine Copulas for Multivariate Dependence. (Unpublished)

Simm, GNC and Hernández-Lobato, JM A Generative Model for Molecular Distance Geometry. (Unpublished)

Kusner, MJ and Paige, B and Hernández-Lobato, JM Grammar Variational Autoencoder. (Unpublished)

Gong, W and Tschiatschek, S and Turner, R and Nowozin, S and Hernández-Lobato, JM and Zhang, C Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. (Unpublished)

Foong, AYK and Li, Y and Hernández-Lobato, JM and Turner, RE 'In-Between' Uncertainty in Bayesian Neural Networks. (Unpublished)

Overweg, H and Popkes, A-L and Ercole, A and Li, Y and Hernández-Lobato, JM and Zaykov, Y and Zhang, C Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. (Unpublished)

Bradshaw, J and Paige, B and Kusner, MJ and Segler, MHS and Hernández-Lobato, JM A Model to Search for Synthesizable Molecules. (Unpublished)

Hernández-Lobato, D and Hernández-Lobato, JM and Li, Y and Bui, T and Turner, RE Stochastic Expectation Propagation for Large Scale Gaussian Process Classification. (Unpublished)

Janz, D and Hron, J and Mazur, P and Hofmann, K and Hernández-Lobato, JM and Tschiatschek, S Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. (Unpublished)

August, M and Hernández-Lobato, JM Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control. (Unpublished)

Depeweg, S and Hernández-Lobato, JM and Doshi-Velez, F and Udluft, S Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables. (Unpublished)

Conference or Workshop Item

Bradshaw, J and Kusner, MJ and Paige, B and Segler, MHS and Hernández-Lobato, JM (2019) Generating molecules via chemical reactions. In: UNSPECIFIED.

Reagen, B and Hernandez-Lobato, JM and Adolf, R and Gelbart, M and Whatmough, P and Wei, GY and Brooks, D (2017) A case for efficient accelerator design space exploration via Bayesian optimization. In: ACM/IEEE International Symposium on Low Power Electronics and Design, 2017-7-24 to 2017-7-26, Taipei, Taiwan.

Jaques, N and Gu, S and Bahdanau, D and Hernández-Lobato, JM and Turner, RE and Eck, D (2017) Sequence tutor: Conservative fine-tuning of sequence generation models with KL-control. In: UNSPECIFIED pp. 2587-2596..

Reagen, B and Whatmough, P and Adolf, R and Rama, S and Lee, H and Lee, SK and Hernandez-Lobato, JM and Wei, GY and Brooks, D (2016) Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. In: UNSPECIFIED pp. 267-278..

Sharmanska, V and Hernandez-Lobato, D and Hernandez-Lobato, JM and Quadrianto, N (2016) Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. In: UNSPECIFIED pp. 2194-2202..

Bui, TD and Hernández-Lobato, JM and Hernández-Lobato, D and Li, Y and Turner, RE (2016) Deep Gaussian processes for regression using approximate expectation propagation. In: UNSPECIFIED pp. 2187-2208..

Hernández-Lobato, JM and Adams, RP (2015) Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: UNSPECIFIED pp. 1861-1869..

Li, Y and Hernández-Lobato, JM and Turner, RE (2015) Stochastic expectation propagation. In: UNSPECIFIED pp. 2323-2331..

Hernández-Lobato, D and Hernández-Lobato, JM and Ghahramani, Z (2015) A probabilistic model for dirty multi-task feature selection. In: UNSPECIFIED pp. 1073-1082..

Houlsby, N and Hernández-Lobato, JM and Ghahramani, Z (2014) Cold-start active learning with robust ordinal matrix factorization. In: UNSPECIFIED pp. 1964-1972..

Hernández-Lobato, JM and Houlsby, N and Ghahramani, Z (2014) Probabilistic matrix factorization with non-random missing data. In: UNSPECIFIED pp. 3394-3436..

Hernández-Lobato, JM and Houlsby, N and Ghahramani, Z (2014) Stochastic inference for scalable probabilistic modeling of binary matrices. In: UNSPECIFIED pp. 1693-1710..

Hernández-Lobato, JM and Lloyd, JR and Hernández-Lobato, D (2013) Gaussian process conditional copulas with applications to financial time series. In: UNSPECIFIED.

Hernández-Lobato, D and Hernández-Lobato, JM (2013) Learning feature selection dependencies in multi-task learning. In: UNSPECIFIED.

Lopez-Paz, D and Hernández-Lobato, JM and Schölkopf, B (2012) Semi-supervised domain adaptation with non-parametric copulas. In: UNSPECIFIED pp. 665-673..

Hernändez-Lobato, JM and Morales-Mombiela, P and Suärez, A (2011) Gaussianity measures for detecting the direction of causal time series. In: UNSPECIFIED pp. 1318-1323..

Hernández-Lobato, D and Hernández-Lobato, JM and Helleputte, T and Dupont, P (2010) Expectation propagation for bayesian multi-task feature selection. In: UNSPECIFIED pp. 522-537..

Hernández-Lobato, JM and Dijkstra, TMH (2010) Hub gene selection methods for the reconstruction of transcription networks. In: UNSPECIFIED pp. 506-521..

Hernández-Lobato, JM and Dijkstra, T and Heskes, T (2009) Regulator discovery from gene expression time series of malaria parasites: A hierarchical approach. In: UNSPECIFIED.

Hernandez-Lobato, JM and Hernandez-Lobato, D and Suarez, A (2007) GARCH processes with non-parametric innovations for market risk estimation. In: UNSPECIFIED pp. 718-727..

Hernandez-Lobato, JM and Suarez, A (2006) Competitive and collaborative mixtures of experts for financial risk analysis. In: UNSPECIFIED pp. 691-700..

Hernandez-Lobato, D and Hernandez-Lobato, JM and Ruiz-Torrubiano, R and Valle, A (2006) Pruning adaptive boosting ensembles by means of a genetic algorithm. In: UNSPECIFIED pp. 322-329..

Janz, D and Westhuizen, JVD and Hernández-Lobato, JM Actively Learning what makes a Discrete Sequence Valid. In: UNSPECIFIED. (Unpublished)

Bui, TD and Hernández-Lobato, JM and Li, Y and Hernández-Lobato, D and Turner, RE Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation. In: UNSPECIFIED. (Unpublished)

Ma, C and Li, Y and Hernández-Lobato, JM Variational Implicit Processes. In: UNSPECIFIED. (Unpublished)

This list was generated on Thu Dec 12 18:16:51 2019 GMT.