García-Ortegón M., Simm G. N. C., Tripp A. J., Hernández-Lobato J. M., Bender A. and Bacallado S. DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design,
Journal of Chemical Information and Modeling, 62, 15, 3486–3502, 2022. [pdf]
Havasi M., Snoek J., Tran D., Gordon J. and Hernández-Lobato J. M. Sampling the Variational Posterior with Local Refinement,
Entropy, 23(11), 1475, 2021. [pdf]
Gordon J. and Hernández-Lobato J. M. Combining Deep Generative and Discriminative Models for Bayesian Semi-Supervised Learning,
Pattern Recognition, Volume 100, April 2020, 107156. [pdf]
Griffiths R.-R. and Hernández-Lobato J. M. Constrained Bayesian optimization for Automatic Chemical Design Using Variational Autoencoders,
Chemical Science, 2019, DOI: 10.1039/c9sc04026a. [pdf]
Bhardwaj K., Havasi M., Yao Y., Brooks D. M., Hernández-Lobato J. M. and Wei G.-Y. Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization,
IEEE Computer Architecture Letters, 18(2):119−123, 2019. [pdf]
*Gómez-Bombarelli R., *Wei J., *Duvenaud D., *Hernández-Lobato J. M., Sánchez-Lengeling B., Sheberla D., Aguilera-Iparraguirre J., Hirzel T., Adam R. P. and Aspuru-Guzik A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules,
ACS Central Science, 2018 [pdf][python code] *Equal contributors.
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
Antoran J., Padhy S., Barbano R., Nalisnick E., Janz D. and Hernández-Lobato J. M. Sampling-based inference for large linear models, with application to linearised Laplace,
In ICLR, 2023.
Midgley L. I., Stimper V., Simm G. N. C., Schölkopf B. and Hernández-Lobato J. M. Flow Annealed Importance Sampling Bootstrap,
In ICLR, 2023.
Chen W., Tripp A. and Hernández-Lobato J. M. Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction,
In ICLR, 2023.
Peis I., Ma C. and Hernández-Lobato J. M. Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo,
In NeurIPS, 2022.
Flamich G., Markou S. and Hernández-Lobato J. M. Fast Relative Entropy Coding with A* coding,
In ICML, 2022.
Antoran J., Janz D., Allingham J., Daxberger E., Barbano R., Nalisnick E., and Hernández-Lobato J. M. Adapting the Linearised Laplace Model Evidence for Modern Deep Learning,
In ICML, 2022.
Huang B., Lu C., Leqi L., Hernandez-Lobato J. M., Glymour C., Schölkopf C. and Zhang K. Action-Sufficient State Representation Learning for Control with Structural Constraints,
In ICML, 2022.
Lu C., Wu Y., Hernández-Lobato J. M and Schölkopf B. Invariant Causal Representation Learning for Out-of-Distribution Generalization,
In ICLR, 2022.
Ross C. M. , Oldewage E. T. and Hernández-Lobato J. M. Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation,
In ICLR, 2022.
He W., Mao X., Ma C., Huang Y., Hernández-Lobato J. M. and Chen T. BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis,
In The Web Conference (WWW), 2022. [pdf]
Murray C., Allingham J. U., Antorán J. and Hernández-Lobato J. M. Addressing Bias in Active Learning with Depth Uncertainty Networks… or Not,
In Proceedings on “I (Still) Can’t Believe It’s Not Better!” at NeurIPS 2021 Workshops, PMLR 163:59-63, 2022. [pdf]
Stimper V., Schölkopf B. and Hernández-Lobato J. M. Resampling Base Distributions of Normalizing Flows,
In AISTATS 2022.
Notin P., Hernández-Lobato J. M. and Gal Y. Improving black-box optimization in VAE latent space using decoder uncertainty,
In NeurIPS 2021.
Ma C. and Hernández-Lobato J. M. Functional Variational Inference based on Stochastic Process Generators,
In NeurIPS 2021.
Campbell A., Chen W., Stimper V., Hernández-Lobato J. M. and Zhang Y. A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization,
In ICML 2021. [pdf]
Gong W., Zhang K., Li Y. and Hernández-Lobato J. M. Active Slices for Sliced Stein Discrepancy,
In ICML 2021. [pdf]
Daxberger E., Nalisnick E., Allingham J., Antoran J. and Hernández-Lobato J. M. Bayesian Deep Learning via Subnetwork Inference,
In ICML 2021. [pdf]
Nalisnick E., Gordon J. and Hernández-Lobato J. M. Predictive Complexity Priors,
In AISTATS 2021. [pdf]
Morales-Alvarez P., Hernández-Lobato D., Molina R. and Hernández-Lobato J. M. Activation-level uncertainty in deep neural networks,
In ICLR 2021. [pdf]
Simm G. N. C., Pinsler R., Csányi G. and Hernández-Lobato J. M. Symmetry-Aware Actor-Critic for 3D Molecular Design,
In ICLR 2021. [pdf]
Gong W., Li Y. and Hernández-Lobato J. M. Sliced Kernelized Stein Discrepancy,
In ICLR 2021. [pdf]
Antoran J. , Bhatt U., Adel T., Weller A. and Hernández-Lobato J. M. Getting a CLUE: A Method for Explaining Uncertainty Estimates,
In ICLR, 2021. [pdf]
Wang Z., Tschiatschek S., Woodhead S., Hernández-Lobato J. M, Peyton Jones S., Baraniuk R. G. and Zhang C. Educational Question Mining At Scale: Prediction, Analysis and Personalization,
In EAAI, 2021. [pdf]
Ma C., Tschiatschek S., Turner R. E., Hernández-Lobato J. M. and Zhang C. VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data,
In NeurIPS, 2020. [pdf]
Bradshaw J., Paige B., Kusner M. J., Segler M. H. S. and Hernández-Lobato J. M. Barking up the right tree: an approach to search over molecule synthesis DAGs,
In NeurIPS, 2020. [pdf]
Antoran J., Allingham J. and Hernández-Lobato J. M. Depth Uncertainty in Neural Networks,
In NeurIPS, 2020. [pdf]
Flamich G., and Havasi M. and Hernández-Lobato J. M. Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding,
In NeurIPS, 2020. [pdf]
Tripp A., Daxberger E. and Hernández-Lobato J. M. Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining,
In NeurIPS, 2020. [pdf]
Simm G. N., Pinsler R. and Hernández-Lobato J. M. Reinforcement Learning for Molecular Design Guided by Quantum Mechanics,
In ICML, 2020. [pdf][supp][python code]
Simm G. N., and Hernández-Lobato J. M. A Generative Model for Molecular Distance Geometry,
In ICML, 2020. [pdf][supp][python code]
Bhardwaj K., Havasi M., Yao Y., Brooks D. M., Hernández-Lobato J. M. and Wei G.-Y. A Comprehensive Methodology to Determine Optimal Coherence Interfaces for Many-Accelerator SoCs,
In ISLPED, 2020.
Janz D., Hron J., Mazur P., Hofmann K., Hernández-Lobato J. M. and Tschiatschek S. Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning,
In NeurIPS, 2019. [pdf]
Pinsler R., Gordon J., Nalisnick E. and Hernández-Lobato J. M. Bayesian Batch Active Learning as Sparse Subset Approximation,
In NeurIPS, 2019. [pdf]
Gong W., Tschiatschek S., Nowozin S., Turner R. E., Hernández-Lobato J. M. and Zhang C. Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model,
In NeurIPS, 2019. [pdf]
Bradshaw J., Kusner M. J., Paige B., Segler M. H. S. and Hernández-Lobato J. M. A Model to Search for Synthesizable Molecules,
In NeurIPS, 2019. [pdf]
Ma C., Li Y. and Hernández-Lobato J. M. Variational Implicit Processes,
In ICML, 2019. [pdf][supp][python code]
Ma C., Tschiatschek S., Palla K., Hernández-Lobato J. M., Nowozin S. and Zhang C. EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE,
In ICML, 2019. [pdf][supp][python code]
Nalisnick E., Hernández-Lobato J. M. and Smyth P. Dropout as a Structured Shrinkage Prior,
In ICML, 2019. [pdf][supp][python code]
Wu A., Nowozin S., Meeds E., Turner R. E., Hernández-Lobato J. M and Gaunt A. L. Deterministic Variational Inference for Robust Bayesian Neural Networks,
In ICLR, 2019. [pdf]
Bradshaw J., Kusner M. J., Paige B., Segler M. H. S. and Hernández-Lobato J. M. A Generative Model For Electron Paths,
In ICLR, 2019. [pdf]
Havasi M., Perhaz R. and Hernández-Lobato J. M. Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters,
In ICLR, 2019. [pdf]
Gong W., Li Y. and Hernández-Lobato J. M. Meta-Learning For Stochastic Gradient MCMC,
In ICLR, 2019. [pdf]
Havasi M., Hernández-Lobato J. M. and Murillo-Fuentes J. J. Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo,
In NeurIPS, 2018. [pdf][python code]
Depeweg S., Hernández-Lobato J. M., Doshi-Velez F. and Udluft S. Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning,
In ICML, 2018. [pdf]
Janz D., van der Westhuizen J., Paige B., Kusner M. and Hernández-Lobato J. M. Learning a Generative Model for Validity in Complex Discrete Structures,
In ICLR, 2018. [pdf][python code]
August M. and Hernández-Lobato J. M. Taking Gradients Through Experiments: LSTMs and Memory Proximal Policy Optimization for Black-Box Quantum Control,
In High Performance Computing – ISC High Performance 2018 International Workshops, Revised Selected Papers, Lecture Notes in Computer Science, volume 11203, 2018. [pdf]
Depeweg S., Hernández-Lobato J. M., Udluft S. and Runkler T. Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks,
In ESANN, 2018. [pdf]
Reagen B., Hernández-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][python code]
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]
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]
José Miguel Hernández-Lobato 