Zhang F., Midgley L. and Hernández-Lobato J. M. Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models, Transactions of Machine Learning Research (J2C Certification), 2025.[pdf]
Formica F. A., Wu E.., Brey L., Gutiérrez D. P., Barbano R., Tribukait H., Hernández-Lobato J. M., Laveille P., R. M Loïc Bridging Innovation and Efficiency: The Promises and Challenges of Self-Driving Labs as Sustainable Drivers for Chemistry, CHIMIA, 79, No. 9, 2025.[pdf]
Fromer J., Wang R., Manjrekar M., Tripp A., Hernández-Lobato J. M. and Coley C. Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum, Journal of Chemical Information and Modeling (in press), 2025.[pdf]
Singh S. and Hernández-Lobato J. M. Bayesian Meta–Learningfor Few–Shot Reaction Outcome Predictionof Asymmetric Hydrogenation of Olefins,
Angewandte Chemie (in press), 2025. [pdf]
Sabanza-Gil V., Pacheco Gutiérrez D., Luterbacher J. S., Barbano R., Hernández-Lobato J. M., Schwaller P. and Roch L. Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research,
Nature Computational Science (in press), 2025.
Singh S. and Hernández-Lobato J. M. A Meta-learning Approach for Selectivity Prediction in Asymmetric Catalysis,
Nature Communications, 16, 3599, 2025. [pdf]
Singh S. and Hernández-Lobato J. M. Data-Driven Insights on Transition Metal-Catalyzed Asymmetric Hydrogenation of Olefins,
The Journal of OrganicChemistry , 89, 17, 12467–12478, 2024. [pdf]
Chen W., Horwood J., Heo J. and Hernández-Lobato J. M. Leveraging Task Structures for Improved Identifiability in Neural Network Representations,
Transactions of Machine Learning Research, 2024. [pdf]
Singh S. and Hernández-Lobato J. M. Deep Kernel Learning for Reaction Outcome Prediction and Optimization,
Communications Chemistry, 7, 136, 2024. [pdf]
Barbano R., Antoran J., Leuschner J., Hernández-Lobato J. M., Jin B. and Kereta Z. Image Reconstruction via Deep Image Prior Subspaces,
Transactions of Machine Learning Research, 2024. [pdf]
P. Morales-Álvarez, A. Schmidt, J.M. Hernández-Lobato, R. Molina Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images,
Pattern Recognition, 0031-3203, 146, 110057, 2024. [pdf]
Antoran J., Barbano R., Leuschner J., Hernández-Lobato J. M. and Jin B. Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior,
Transactions of Machine Learning Research, 2023. [pdf]
Daxberger E., Swaroop S., Osawa K., Yokota R., Turner R. E., Hernández-Lobato J. M. and Khan M. E. Improving Continual Learning by Accurate Gradient Reconstructions of the Past,
Transactions of Machine Learning Research, 2023. [pdf]
Stimper V., Liu D., Campbell A., Berenz V., Ryll R., Schölkopf B and Hernández-Lobato J. M. normflows: A PyTorch Package for Normalizing Flows,
Journal of Open Source Software, 8(86), 5361, 2023. [pdf]
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
He J., Du Y., Vargas F., Wang Y., Gomes C. P., Hernández-Lobato J. M. and Vanden-Eijnden E. FEAT: Free energy Estimators with Adaptive Transport,
In NeurIPS, 2025.
Song Z., Li M., Zhang Y., King I. and Hernández-Lobato J. M. Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time,
In KDD, 2025.
Song Z., Meng Z., Hernández-Lobato J. M. Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space,
In ICML, 2025.
Lin J. A., Ament S., Balandat M., Eriksson D., Hernández-Lobato J. M. and Bakshy E. Scalable Gaussian Processes with Latent Kronecker Structure,
In ICML, 2025.
Rissanen S., OuYang R., He J., Chen W., Heinonen M., Solin A., Hernández-Lobato J.M. Progressive Tempering Sampling with Diffusion,
In ICML, 2025.
Almudévar A., Hernández-Lobato J. M., Khurana S., Marxer R. and Ortega A. Aligning Multimodal Representations through an Information Bottleneck,
In ICML, 2025.
Bergna R., Calvo Ordoñez S., Opolka F., Lio P. and Hernández-Lobato J. M. Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations,
In ICLR, 2025.
He J., Chen W., Zhang M., Barber D. and Hernández-Lobato J. M. Training Neural Samplers with Reverse Diffusive KL Divergence,
In AISTATS, 2025.
Lin J. A., Padhy S., Mlodozeniec B. K., Antoran J. and Hernández-Lobato J. M. Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes,
In NeurIPS, 2024. [pdf]
Shysheya A., Diaconu C., Bergamin F., Perdikaris P., Hernández-Lobato J. M., Turner R. E. and Mathieu E. On conditional diffusion models for PDE simulations,
In NeurIPS, 2024. [pdf]
He J., Flamich G., Hernández-Lobato J. M. Accelerating Relative Entropy Coding with Space Partitioning,
In NeurIPS, 2024. [pdf]
Allingham J. U., Mlodozeniec B. K, Padhy S., Antoran J., Krueger D., Turner R. E., Nalisnick E. and Hernández-Lobato J. M. A Generative Model of Symmetry Transformations,
In NeurIPS, 2024. [pdf]
Papamarkou T., Skoularidou M., Palla K., Aitchison L., Arbel J., Dunson D., Filippone M., Fortuin V., Hennig P., Hernández-Lobato J. M., Hubin A., Immer A., Karaletsos T., Khan M. E., Kristiadi A., Li Y., Mandt S., Nemeth C., Osborne M. A., Rudner T. G. J., Rügamer D., Teh Y. W., Welling M., Wilson A. G. and Zhang R. Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI,
In ICML, 2024. [pdf]
Clarke R. M. and Hernández-Lobato J. M. Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens,
In ICML, 2024. [pdf]
Chen X., Cai R., TingHuang Z., Zhu Y., Horwood J., Hao Z., Li Z. and Miguel Hernández-Lobato J. M. Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation,
In ICML, 2024. [pdf]
Chen W., Zhang M., Paige B., Hernández-Lobato J. M. and Barber D. Diffusive Gibbs Sampling,
In ICML, 2024. [pdf]
Lin J. A., Padhy S., Antoran J., Tripp A., Terenin A., Szepesvari C., Hernández-Lobato J. M. and Janz D. Stochastic Gradient Descent for Gaussian Processes Done Right,
In ICLR, 2024. [pdf]
He J., Flamich G., Guo Z. and Hernández-Lobato J. M. RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations,
In ICLR, 2024. [pdf]
Tripp A., Maziarz K., Lewis S., Segler M. and Hernández-Lobato J. M. Retro-fallback: retrosynthetic planning in an uncertain world,
In ICLR, 2024. [pdf]
Tripp A., Bacallado S., Singh S. and Hernández-Lobato J. M. Tanimoto Random Features for Scalable Molecular Machine Learning,
In NeurIPS, 2023. [pdf]
Guo Z., Flamich G., He J., Zhibo Chen Z., and Hernández-Lobato J. M. Compression with Bayesian Implicit Neural Representations,
In NeurIPS, 2023. [pdf]
Flamich G., Markou S. and Hernández-Lobato J. M. Faster Relative Entropy Coding with Greedy Rejection Coding,
In NeurIPS, 2023. [pdf]
Lin J. A., Antoran J., Padhy S., Janz D., Hernández-Lobato J. M. and Terenin A. Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent,
In NeurIPS, 2023. [pdf]
Midgley L. I., Stimper V., Antoran J., Mathieu E., Schölkopf B. and Hernández-Lobato J. M. SE(3) Equivariant Augmented Coupling Flows,
In NeurIPS, 2023. [pdf]
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. [pdf]
Midgley L. I., Stimper V., Simm G. N. C., Schölkopf B. and Hernández-Lobato J. M. Flow Annealed Importance Sampling Bootstrap,
In ICLR, 2023. [pdf]
Chen W., Tripp A. and Hernández-Lobato J. M. Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction,
In ICLR, 2023. [pdf]
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. [pdf]
Flamich G., Markou S. and Hernández-Lobato J. M. Fast Relative Entropy Coding with A* coding,
In ICML, 2022. [pdf]
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. [pdf]
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. [pdf]
Lu C., Wu Y., Hernández-Lobato J. M and Schölkopf B. Invariant Causal Representation Learning for Out-of-Distribution Generalization,
In ICLR, 2022. [pdf]
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. [pdf]
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 