Publications and Code

Working papers

  • *Gómez-Bombarelli R., *Duvenaud D., *Hernández-Lobato J. M., Aguilera-Iparraguirre J., Hirzel T. D., Adams R. P. and Aspuru-Guzik A.

    Automatic chemical design using a data-driven continuous representation of molecules,
    arXiv:1610.02415, 2016. [pdf] [python code]
    *Equal contributors.

Journals

  • Hernández Lobato J. M., Gelbart M. A., Adams R. P, Hoffman M. W. and Ghahramani Z.
    A General Framework for Constrained Bayesian Optimization using Information-based Search,
    Journal of Machine Learning Research, 17(160):1−53, 2016. [pdf] [python code]
  • Hernández-Lobato J. M., Hernández-Lobato D. and Suárez A.
    Expectation Propagation in Linear Regression Models with Spike-and-slab Priors,
    Machine Learning, 99.3: 437−487, 2015. [pdf] [R code]
  • Hernández-Lobato D., Hernández-Lobato J. M. and Dupont P.
    Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation,
    Journal of Machine Learning Research, 14:1891−1945, 2013. [pdf] [R code]
  • Hernández-Lobato J. M. and Suárez A.
    Semiparametric Bivariate Archimedean Copulas,
    Computational Statistics & Data Analysis, 55(6), 2038–2058, 2011. [pdf]
  • Hernández-Lobato J. M., Hernández-Lobato D. and Suárez A.
    Network-based Sparse Bayesian Classification,
    Pattern Recognition, 44(4), 886–900, 2011. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M. and Suárez A.
    Expectation Propagation for Microarray Data Classification,
    Pattern Recognition Letters, 31(12), 1618–1626, 2010. [pdf] [R code]
  • Hernández-Lobato D. and Hernández-Lobato J. M.
    Bayes Machines for Binary Classification,
    Pattern Recognition Letters, 29(10), 1466–1473, 2008. [pdf]

Patents

  • Shastri L., Gharamani Z., Hernández-Lobato J. M., Kanagasabapathi B. and Raj K. S. A. A. D.
    Method and system for mining frequent and in-frequent items from a large transaction database,
    US Patent App. 14/493,706, 2015. [link]

Conferences

  • Reagen B., Hernánez-Lobato J. M., Adolf R., Gelbart M. A., Whatmough P., Brooks D. and Wei G.-Y.
    A Case for Efficient Accelerator Design Space Exploration via Bayesian Optimization,

    In ISLPED, 2017. [pdf]

  • Kusner M. J., Paige B. and Hernández-Lobato J. M.
    Grammar Variational Autoencoder,
    In ICML , 2017. [pdf]
  • Hernández-Lobato J. M., Requeima J.,  Pyzer-Knapp E. O. and Aspuru-Guzik A.
    Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space,
    In ICML, 2017. [pdf]
  • Jaques N., Gu S., Bahdanau D., Hernández-Lobato J. M., Turner R. E. and Eck D.
    Sequence Tutor: Conservative fine-tuning of sequence generation models with KL-control,
    In ICML, 2017. [pdf]
  • Depeweg S., Hernández-Lobato J. M., Doshi-Velez F. and Udluft S.
    Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks,
    In ICLR, 2017. [pdf] [python code] [talk]
  • Hernández-Lobato J. M., Li Y., Rowland  M., Bui T. D., Hernández-Lobato D. and Turner R. E.
    Black-Box Alpha Divergence Minimization,
    In ICML, 2016. [pdf] [python code] [talk]
  • Bui T. D.,  Hernández-Lobato J. M.,  Li Y., Hernández-Lobato D. and Turner R. E.
    Deep Gaussian Processes for Regression using Approximate Expectation Propagation,
    In ICML, 2016. [pdf] [python code]
  • Hernández-Lobato D., Hernández-Lobato J. M., Shah A. and R. P. Adams.
    Predictive Entropy Search for Multi-objective Bayesian Optimization,
    In ICML, 2016. [pdf] [Spearmint code] [blog] [talk]
  • Sharmanska V., Hernández-Lobato D., Hernández-Lobato J. M. and Quadrianto N.
    Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations,
    In CVPR, 2016. [pdf] [supp. material]
  • Reagen B., Whatmough P. Adolf R., Rama S., Lee H., Lee S., Hernandez-Lobato J. M., Wei G. Y. and Brooks D.
    Minerva: Enabling Low-Power, High-Accuracy Deep Neural Network Accelerators,
    In ISCA, 2016. [pdf]
  • Hernández-Lobato D. and Hernández-Lobato J. M.
    Scalable Gaussian Process Classification via Expectation Propagation,
    In AISTATS, 2016. [pdf]
  • Li Y., Hernández-Lobato J. M. and Turner R. E.
    Stochastic Expectation Propagation,
    In NIPS, 2015. [pdf] [python code]
  • Hernández-Lobato J. M. and Adams R.
    Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks,
    In ICML, 2015. [pdf] [supp. material] [C and theano code]
  • Hernández-Lobato J. M., Gelbart A. M., Hoffman M. W., Adams R. and Ghahramani Z.
    Predictive Entropy Search for Bayesian Optimization with Unknown Constraints,
    In ICML, 2015. [pdf] [supp. material] [python code]
  • Hernández-Lobato D., Hernández-Lobato J. M. and Ghahramani Z.
    A Probabilistic Model for Dirty Multi-task Feature Selection,
    In ICML, 2015. [pdf] [supp. material] [R code]
  • Hernández-Lobato J. M., Hoffman M. W. and Ghahramani Z.
    Predictive Entropy Search for Efficient Global Optimization of Black-box Functions,
    In NIPS, 2014. [pdf] [supp. material] [matlab code]
  • Wu Y., Hernández-Lobato J. M. and Ghahramani Z.
    Gaussian Process Volatility Model,
    In NIPS, 2014. [pdf] [matlab code]
  • Hernández-Lobato J. M., Houlsby N. and Ghahramani Z.
    Probabilistic Matrix Factorization with Non-random Missing Data,
    In ICML, 2014. [pdf] [supp. material] [R code and NIPS dataset] [talk video]
  • Houlsby N., Hernández-Lobato J. M. and Ghahramani Z.
    Cold-start Active Learning with Robust Ordinal Matrix Factorization,
    In ICML, 2014. [pdf] [supp. material] [R code] [talk video]
  • Hernández-Lobato J. M., Houlsby N. and Ghahramani Z.
    Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices,
    In ICML, 2014. [pdf] [supp. material] [R code] [talk video]
  • Hernández-Lobato J. M., Lloyd J. R. and Hernández-Lobato D.
    Gaussian Process Conditional Copulas with Applications to Financial Time Series,
    In NIPS, 2013. [pdf] [data and R code] [R code only]
  • Hernández-Lobato D. and Hernández-Lobato J. M.
    Learning Feature Selection Dependencies in Multi-task Learning,
    In NIPS, 2013. [pdf] [R code and supp. material]
  • Wu Y., Hernández-Lobato J. M. and Ghahramani Z.
    Dynamic Covariance Models for Multivariate Financial Time Series,
    In ICML, 2013. [pdf] [matlab code]
  • Lopez-Paz D., Hernández-Lobato J. M. and Ghahramani Z.
    Gaussian Process Vine Copulas for Multivariate Dependence,
    In ICML, 2013. [pdf] [R code]
  • Kaschesky M., Sobkowicz P., Hernández-Lobato J. M., Bouchard G., Archambeau C., Scharioth N., Manchin R., Gschwend A. and Riedl R.
    Bringing Representativeness into Social Media Monitoring and Analysis,
    In HICSS, 2013. [pdf]
  • Houlsby N., Hernández-Lobato J. M., Huszar F. and Ghahramani Z.
    Collaborative Gaussian Processes for Preference Learning,
    In NIPS, 2012. [pdf] [R code]
  • Lopez-Paz D., Hernández-Lobato J. M. and Schölkopf B.
    Semi-Supervised Domain Adaptation with Non-Parametric Copulas,
    In NIPS, 2012. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M. and Dupont P.
    Robust Multi-Class Gaussian Process Classification,
    In NIPS, 2011. [pdf] [R code and sup. material]
  • Hernández-Lobato J. M., Morales-Mombiela P. and Suárez A.
    Gaussianity Measures for Detecting the Direction of Causal Time Series,
    In IJCAI, 2011. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Helleppute T. and Dupont P.
    Expectation Propagation for Bayesian Multi-task Feature Selection,
    In ECML PKDD, 2010. [pdf]
  • Hernández-Lobato J. M. and Dijkstra T. M. H.
    Hub Gene Selection Methods for the Reconstruction of Transcription Networks,
    In ECML PKDD, 2010. [pdf]
  • Hernández-Lobato J. M., Hernández-Lobato D. and Suárez A.
    GARCH Processes with Non-parametric Innovations for Market Risk Estimation,
    In ICANN, 2007. [pdf]
  • Hernández-Lobato J. M., Dijkstra T. and Heskes T.
    Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierarchical Approach,
    In NIPS, 2007. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Ruiz-Torrubiano R. and Valle A.
    Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm,
    In IDEAL, 2006. [pdf]
  • Hernández-Lobato J. M. and Suárez A.
    Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis,
    In ICANN, 2006.  [pdf]

Workshop Abstracts

  • Kusner M. and Hernandez-Lobato J. M.
    GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution,
    In NIPS Workshop on Adversarial Training, Barcelona, Spain 2016. [pdf]
  • Gomez-Bombarelli R., Duvenaud D., Hernández-Lobato J. M., Hirzel T., Aguilera-Iparraguirre J., Adams R. P. and Aspuru-Guzik A.
    Automatic Chemical Design using Variational Autoencoders,
    In NIPS Workshop on Constructive Machine Learning, Barcelona, Spain 2016. [pdf]
  • Depeweg S., Hernández-Lobato J. M., Doshi-Velez F. and Udluft S.
    Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks,
    In NIPS Workshop on Deep Reinforcement Learning, Barcelona, Spain, 2016. [pdf]
  • Hernández-Lobato D., Bui T. D., Li Y., Hernández-Lobato J. M. and Turner R. E.
    Importance Weighted Autoencoders with Uncertain Neural Network Parameters,
    In NIPS Workshop on Bayesian Deep Learning, Barcelona, Spain, 2016. [pdf]
  • Bui T. D., Hernández-Lobato D., Hernández-Lobato J. M., Li Y. and Turner R. E.
    Black-box α-divergence for Deep Generative Models,
    In NIPS Workshop on Advances in Approximate Bayesian Inference, Barcelona, Spain, 2016. [pdf]
  • Hernandez-Lobato J. M., Pyzer-Knapp E., Aspuru-Guzik A. and Adams R. P.
    Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space,
    In NIPS Workshop on Bayesian Optimization, Barcelona, Spain, 2016. [pdf]
  • Hernández-Lobato J. M., Gelbart M. A., Reagen B., Adolf R., Hernández-Lobato D., Whatmough P., Brooks D., Wei G.-Y. and Adams R. P.
    Designing Neural Network Hardware Accelerators with Decoupled Objective Evaluations,
    In NIPS Workshop on Bayesian Optimization, Barcelona, Spain, 2016. [pdf]
  • Schulz E.,Speekenbrink M., Hernández-Lobato J.M.,  Ghahramani Z. and Gershman, S.
    Quantifying mismatch in Bayesian optimization,
    In NIPS Workshop on Bayesian Optimization, Barcelona, Spain, 2016. [pdf]
  • Hernández-Lobato J. M., Li Y., Hernández-Lobato D., Bui T. and Turner R. E.
    Black-box alpha-divergence minimization,
    In NIPS Workshop on Black-box Learning and Inference, Montreal, Canada, 2015. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Shah A. and Adams R. P.
    Predictive Entropy Search for Multi-objective Bayesian Optimization,
    In NIPS Workshop on Bayesian Optimization: Scalability and Flexibility, Montreal, Canada, 2015. [pdf]
  • Bui T. D., Hernández-Lobato J. M., Li Y., Hernández-Lobato D. and Turner R. E.
    Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation,
    In NIPS Workshop on Advances in Approximate Bayesian Inference, Montreal, Canada, 2015. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Li Y., Bui T. D., and Turner R. E.
    Stochastic Expectation Propagation for Large Scale Gaussian Process Classification,
    In NIPS Workshop on Advances in Approximate Bayesian Inference, Montreal, Canada, 2015. [pdf]
  • Hernández-Lobato J. M., Gelbart M. A., Hoffman M. W., Adams R. P. and Ghahramani Z.
    Predictive Entropy Search for Bayesian Optimization with Unknown Constraints,
    In NIPS Workshop on Bayesian Optimization in Academia and Industry, Montreal, Canada, 2014. [pdf]
  • Hernández-Lobato J. M., Hoffman M. W. and Ghahramani Z.
    Predictive Entropy Search for Efficient Global Optimization of Black-box Functions,
    In NIPS Workshop on Bayesian Optimization in Academia and Industry, Montreal, Canada, 2014. [pdf]
  • Hernández-Lobato J. M., Lloyd J. R., Hernández-Lobato D. and Ghahramani Z.
    Learning the Semantics of Discrete Random Variables: Ordinal or Categorical?,
    In NIPS Workshop on Learning Semantics, Montreal, Canada, 2014. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M. and Ghahramani Z.
    A Probabilistic Model for Dirty Multi-task Feature Selection,
    In NIPS Workshop on Transfer and Multi-Task Learning: Theory meets Practice, Montreal, Canada, 2014. [pdf]
  • Hernández-Lobato J. M., Houlsby N. and Ghahramani Z.
    Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices,
    In NIPS Workshop on Randomized Methods for Machine Learning, Lake Tahoe, Nevada, United States, 2013. [pdf]
  • López-Paz D. and Hernández-Lobato J. M.
    Transfer Learning with Copulas,
    In NIPS Workshop on Copulas in Machine Learning, Granada, Spain, 2011. [pdf]
  • Hernández-Lobato J. M., López-Paz D. and Ghahramani Z.
    Expectation Propagation for the Estimation of Conditional Bivariate Copulas,
    In NIPS Workshop on Copulas in Machine Learning, Granada, Spain, 2011. [pdf]
  • Hernández-Lobato J. M. and Suárez A.
    Modeling Dependence with Semiparametric Archimedean Copulas,
    In International Workshop on Advances in Machine Learning for Computational Finance, London, UK, 2009. [link]

Technical Reports

  • Hernández-Lobato J. M. and Hernández-Lobato D.
    Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors,
    arXiv:1112.2289 [stat.ML], 2011. [pdf]
  • Hernández-Lobato J. M.
    Transcription Networks, Microarray Chips and Sparse Linear Methods,
    Technical report, Universidad Autónoma de Madrid, 2009. [pdf]

Ph.D. Thesis and Master Thesis

  • Ph.D. Thesis, Hernández-Lobato J.M.
    Balancing Flexibility and Robustness in Machine Learning: Semi-parametric Methods and Sparse Linear Models
    Universidad Autonoma de Madrid, 2010. [pdf]
  • M.Phil. Thesis, Hernández-Lobato J.M.
    Time Series Models for Measuring Market Risk,
    Universidad Autonoma de Madrid, 2007. [pdf]
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