Publications

PhD Thesis

  • Hernández-Lobato D.
    Prediction Based on Averages over Automatically Induced Learners: Ensemble Methods and Bayesian Techniques
    Universidad Autónoma de Madrid, January 2010 [pdf]

Journals

  • Hernández-Lobato D., Morales-Mombiela P., Lopez-Paz D., Suárez A.
    Non-linear Causal Inference using Gaussianity Measures.
    Journal of Machine Learning Research, 17, pages 1-39, 2016 [pdf].
  • Lauwerys BR, Hernández-Lobato D, Gramme P, Ducreux J, Dessy A, Focant I, et al.
    Heterogeneity of Synovial Molecular Patterns in Patients with Arthritis
    PLoS ONE 10(4): e0122104, 2015 [pdf]
  • Hernández-Lobato D., Katakis I., Martinez-Muñoz G., Partalas I.
    Special Issue on “Solving complex machine learning problems with ensemble methods”
    Neurocomputing, vo. 150, Part B, pages 402–403, 2015 [pdf]
  • Hernández-Lobato J. M., Hernández-Lobato D., and Suárez A.
    ExpectationPropagation in Linear Regression Models with Spike-and-slab Priors
    Machine Learning, vol. 99, pages 437–487, 2015 [pdf] [R-code]
  • Soto V., Moratilla-García S., Martínez-Muñoz G., Hernández-Lobato D. and Suárez A.
    A Double Pruning Scheme for Boosting Ensembles
    IEEE Transactions on Cybernetics, Volume 44, pages 2682 – 2695, 2014 [pdf]
  • 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(Jul):1891−1945, 2013 [pdf] [R-Code]
  • Hernández-Lobato D., Martínez-Muñoz G. and Suárez A.
    How Large Should Ensembles of Classifiers Be?

    Pattern Recognition. Volume 46, Issue 5, May 2013, Pages 1323–1336 [pdf] [R-Code]
  • Hernández-Lobato D., Martínez-Muñoz G. and Suárez A.
    Empirical Analysis and Evaluation of Approximate Techniques for Pruning Regression Bagging Ensembles
    Neurocomputing. Volume 75, Pages 2250-2264, 2011 [pdf]
  • Hernández-Lobato D., Martínez-Muñoz G. and Suárez A.
    Inference on the Prediction of Ensembles of Infinite Size
    Pattern Recognition. Volume 44, Issue 7, Pages 1426-1434,2011 [pdf]
  • Hernández-Lobato J. M., Hernández-Lobato D. and Suárez A.
    Network-based Sparse Bayesian Classification
    Pattern Recognition, Volume 44, Issue 4, Pages 886-900, 2011. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M. and Suárez A.
    Expectation Propagation for Microarray data Classification
    Pattern Recognition Letters, Volume 31, Issue 12, 1 September 2010, pp. 1618-1626. [pdf] [R-Code]
  • Hernández-Lobato D., Martínez-Muñoz G. and Suárez A.
    Statistical Instance-Based Pruning in Ensembles of Independent Classifiers
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 364-369, February, 2009. [pdf]

  • Martínez-Muñoz G., Hernández-Lobato D. and Suárez A.
    An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 245-259, February, 2009. [pdf]
  • Hernández-Lobato D. and Hernández-Lobato J. M.
    Bayes Machines for Binary Classification
    Pattern Recognition Letters, Volume 29, Issue 10, 15 July 2008, Pages 1466-1473, ISSN 0167-8655.[pdf]
  • Martinez-Munoz G., Sanchez-Martinez A., Hernandez-Lobato D. and Suarez A.
    Class-switching Neural Network Ensembles
    Neurocomputing, Volume 71, Issues 13-15, Artificial Neural Networks (ICANN 2006) / Engineering of Intelligent Systems (ICEIS 2006), August 2008, Pages 2521-2528, ISSN 0925-2312.[pdf]

Conferences

  • Bui T.D., Hernández-Lobato J.M, Hernández-Lobato D., Li Y., Turner R.E.
    Deep Gaussian Processes for Regression using Approximate Expectation Propagation
    International Conference on Machine Learning, 2016 [pdf][Sup. Material]
  • Hernández-Lobato J.M., Li Y., Rowland M., Hernández-Lobato D., Bui T.D., Turner R.E.
    Black-box alpha-divergence Minimization
    International Conference on Machine Learning, 2016 [pdf][Sup. Material]
  • Hernández-Lobato D., Hernández-Lobato J.M., Shah A., Adams R.P.
    Predictive Entropy Search for Multi-objective Bayesian Optimization
    International Conference on Machine Learning, 2016 [pdf][Sup. Material]
  • Sharmanska V., Hernández-Lobato D., Hernández-Lobato J.M., Quadrianto N.
    Ambiguity helps: Classification with disagreements in crowdsourced annotations
    International Conference on Computer Vision and Pattern Recognition, 2016 [pdf]
  • Hernández-Lobato D., Hernández-Lobato J.M.
    Scalable Gaussian Process Classification via Expectation Propagation
    International Conference on Artificial Intelligence and Statistics, 2016 [pdf][R-Code & Sup. Material]
  • Hernández-Lobato D., Hernández-Lobato J.M, Ghahramani Z.
    A Probabilistic Model for Dirty Multi-task Feature Selection
    International Conference on Machine Learning (ICML), Lille, France, 2015 [pdf][R-Code & Sup. Material]
  • Hernández-Lobato D., Sharmanska V., Kersting K., Lambert Ch. and Quadrianto N.
    Mind the Nuisance: Gaussian Process Classication using Privileged Noise
    Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, 2014 [pdf]
  • Hernández-Lobato J. M., LLoyd J., Hernández-Lobato D.
    Gaussian Process Conditional Copulas with Applications to Financial Time Series
    Advances in Neural Information Processing Systems (NIPS), Tahoe, USA, 2013, Pages 1736-1744[pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M.
    Learning Feature Selection Dependencies in Multi-task Learning
    Advances in Neural Information Processing Systems (NIPS), Tahoe, USA, 2013, Pages 746-754[pdf][R-Code & Sup. Material]
  • Morales-Mombiela P., Hernández-Lobato D., and Suárez A.
    Statistical Tests for the Detection of the Arrow of Time in Vector Autoregressive Models
    Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing 2013, pages 1544-1550 [pdf]
  • Helleputte T., Dessy A., Hernandez-Lobato D., Dupont P.,  Lauwerys B.RheumaKit,
    A New Early Diagnostic Tool for Patients with Arthritis
    Winner of the Inspiring Young Scientist Award, Knowledge for Growth, Ghent, Belgium, 30 May 2013.
  • Hernández-Lobato D., Martínez-Muñoz G.,  and Suárez A.
    On the Independence of the Individual Predictions in Parallel Ranzomized Ensembles
    European Symposium on Artificial Neural Networks (ESANN), Bruge, Belgium, 2012, Pages 233-238 [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M.,  and Dupont P.
    Robust Multi-Class Gaussian Process Classification
    Advances in Neural Information Processing Systems (NIPS), Granada, Spain, 2011, Pages 280-288 [pdf] [R-Code & Sup. Material]
  • I. Focant, D. Hernández-Lobato, J. Ducreux, P. Durez, A. Nzeusseu, Toukap, D. Elewaut, F. Houssiau, P. Dupont and B. Lauwerys.
    Feasibility of a Molecular Diagnosis of Arthritis Based on the Identification of Specific Transcriptomic Profiles in Knee Synovial Biopsies
    Belgian Congress on Rheumatology, Ghent, Belgium, 2011.
  • Touleimat N. Hernández-Lobato D., and Dupont P.
    Variance Estimators for t-Test Ranking Influence the Stability and Predictive Performance of Microarray Gene Signatures
    European Conference on Computational Biology (ECCB10), Ghent, Belgium, September 26-29, 2010. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Helleputte T. and Dupont P.
    Expectation Propagation for Bayesian Multi-task Feature Selection
    Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I LNCS 6321, pp. 522-537. [pdf]
  • Soto V., Martínez-Muñoz G., Hernández-Lobato D. and Suárez A.
    A Double Pruning Algorithm for Classification Ensembles
    In Multiple Classifiers Systems (MCS), Cairo, Egypt, April 7-9, 2010, LNCS 5997, ISBN 978-3-642-12126-5, pp. 104-113. [pdf]
  • Martínez-Muñoz G., Hernández-Lobato D. and Suárez A.
    Statistical Instance-based Ensemble Pruning for Multi-class Problems
    In International Conference on Artificial Neural Networks (ICANN), Limassol, Cyprus, 2009, LNCS 5768, ISBN 978-3-642-04273-7, pp. 90-99. [pdf]
  • Hernández-Lobato D.
    Sparse Bayes Machines for Binary Classification
    International Conference on Artificial Neural Networks (ICANN), Prague, Czech Republic, September 3-6, 2008, Part I LNCS 5163 , pp. 205-214, ISBN 978-3-540-87535-2.[pdf]
  • Hernández-Lobato D., Martínez-Muñoz G. and Suárez A.
    Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles
    In 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Birmingham, UK, 2007, LNCS 4881, pp. 47-56, ISBN 978-3-540-77225-5.[pdf]
  • Hernández-Lobato J. M., Hernández-Lobato D. and Suárez A.
    GARCH Processes with Non-parametric Innovations for Market Risk Estimation
    In International Conference on Artificial Neural Networks (ICANN), Porto, Portugal, 2007, Part I, LNCS 4668, pp. 718-727, 2007, ISBN 978-3-540-74689-8. [pdf]
  • Martínez-Muñoz G., Hernández-Lobato D. and Suárez A.
    Selection of Decision Stumps in Bagging Ensembles
    In International Conference on Artificial Neural Networks (ICANN), Porto, Portugal, 2007, Part I, LNCS 4668, pp. 319-328, 2007, ISBN 978-3-540-74689-8. [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 7th Intelligent Data Engineering and Automated Learning (IDEAL), Burgos, Spain, 2006, LNCS 4224, pp. 995-1002, 2006, ISBN 978-3-540-45485-4. [pdf]
  • Martínez-Muñoz G., Sánchez-Martínez A., Hernández-Lobato D. and Suárez A.
    Building Ensembles of Neural Networks with Class-switching
    In International Conference on Artificial Neural Networks (ICANN), Athens, Greece, 2006, Part I, LNCS 4131, pp. 178-187, 2006, ISBN 3-540-38625-4. [pdf]
  • Hernández-Lobato D., Martínez-Muñoz, Suárez A.
    Pruning in Ordered Regression Bagging Ensembles
    In IJCNN 2006, Proceedings of the IEEE World Congress on Computational Intelligence. Vancouver, Canada, pp. 1266-1273. [pdf]

Workshop Abstracts

  • Hernández-Lobato J.M., Gelbart M.A., Reagan B., Adolf R., Hernandez-Lobato D., Whatmough P.N., Brooks D., Wei G.Y., Adams R.P.
    Designing Neural Network Hardware Accelerators with Decoupled Objective Evaluations,
    In NIPS workshop on Bayesian Optimization, 2016. [pdf]
  • Bui T.D., Hernández-Lobato D., Hernández-Lobato J.M., Li Y., Turner R.E.
    Black-box α-divergence for Deep Generative Models,
    In NIPS workshop on Approximate Bayesian Inference, 2016. [pdf]
  • Hernández-Lobato D., Bui T.D., Li Y., Hernández-Lobato J.M., Turner R.E.
    Importance Weighted Autoencoders with Random Neural Network Parameters,
    In NIPS workshop on Bayesian Deep Learning, 2016. [pdf]
  • Garrido-Merchán E.C., Hernández-Lobato D.
    Predictive Entropy Search for Bayesian Multi-objective Optimization with Constraints,
    In NIPS workshop on Bayesian Optimization, 2016. [pdf]
  • Garrido-Merchán E.C., Hernández-Lobato D.
    Predictive Entropy Search for Bayesian Multi-objective Optimization with Constraints,
    In International Workshop on Advances and Applications of Data Science & Engineering, 2016. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Shah A., and Adams R. P.
    Predictive Entropy Search for Bayesian Multi-objective Optimization,
    In NIPS Workshop on Bayesian Optimization, Montreal, Canada, 2015. [pdf]
  • Bui T., Hernández-Lobato J. M., Li Y., Hernández-Lobato D., and Turner R. E.
    Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Back-Propagation,
    In NIPS Workshop on Advances in Approximate Bayesian Inference, Montreal, Canada, 2015. [pdf]
  • Hernández-Lobato J. M., Li Y., Hernández-Lobato D., Bui T. and Turner R. E.
    Black-box Alpha-divergence Minimization,
    In NIPS Workshops on Advances in Approximate Bayesian Inference and
    Black-box Learning and Inference, Montreal, Canada, 2015. [pdf]
  • Hernández-Lobato D., Hernández-Lobato J. M., Li Y., Bui T. 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 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., 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]
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