José Miguel Hernández-Lobato

Orcid: 0000-0001-7610-949X

Affiliations:
  • University of Cambridge, Cambridge, UK


According to our database1, José Miguel Hernández-Lobato authored at least 170 papers between 2006 and 2024.

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Bibliography

2024
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images.
Pattern Recognit., February, 2024

Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks.
Trans. Mach. Learn. Res., 2024

Image Reconstruction via Deep Image Prior Subspaces.
Trans. Mach. Learn. Res., 2024

On conditional diffusion models for PDE simulations.
CoRR, 2024

Training Neural Samplers with Reverse Diffusive KL Divergence.
CoRR, 2024

Batched Bayesian optimization with correlated candidate uncertainties.
CoRR, 2024

Getting Free Bits Back from Rotational Symmetries in LLMs.
CoRR, 2024

Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research.
CoRR, 2024

BEnDEM:A Boltzmann Sampler Based on Bootstrapped Denoising Energy Matching.
CoRR, 2024

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models.
CoRR, 2024

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations.
CoRR, 2024

Diagnosing and fixing common problems in Bayesian optimization for molecule design.
CoRR, 2024

Improving Antibody Design with Force-Guided Sampling in Diffusion Models.
CoRR, 2024

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes.
CoRR, 2024

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes.
CoRR, 2024

Accelerating Relative Entropy Coding with Space Partitioning.
CoRR, 2024

Generative Active Learning for the Search of Small-molecule Protein Binders.
CoRR, 2024

A Generative Model of Symmetry Transformations.
CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR, 2024


Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Diffusive Gibbs Sampling.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Retro-fallback: retrosynthetic planning in an uncertain world.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Stochastic Gradient Descent for Gaussian Processes Done Right.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
normflows: A PyTorch Package for Normalizing Flows.
Dataset, November, 2023

normflows: A PyTorch Package for Normalizing Flows.
J. Open Source Softw., July, 2023

normflows: A PyTorch Package for Normalizing Flows.
Dataset, July, 2023

normflows: A PyTorch Package for Normalizing Flows.
Dataset, June, 2023

normflows: A PyTorch Package for Normalizing Flows.
Dataset, June, 2023

Improving Continual Learning by Accurate Gradient Reconstructions of the Past.
Trans. Mach. Learn. Res., 2023

Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
Trans. Mach. Learn. Res., 2023

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo.
CoRR, 2023

Adam through a Second-Order Lens.
CoRR, 2023

Genetic algorithms are strong baselines for molecule generation.
CoRR, 2023

Graph Neural Stochastic Differential Equations.
CoRR, 2023

Minimal Random Code Learning with Mean-KL Parameterization.
CoRR, 2023

Online Laplace Model Selection Revisited.
CoRR, 2023

Leveraging Task Structures for Improved Identifiability in Neural Network Representations.
CoRR, 2023

Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.
CoRR, 2023

Tanimoto Random Features for Scalable Molecular Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SE(3) Equivariant Augmented Coupling Flows.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Compression with Bayesian Implicit Neural Representations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Faster Relative Entropy Coding with Greedy Rejection Coding.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Flow Annealed Importance Sampling Bootstrap.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Sampling-based inference for large linear models, with application to linearised Laplace.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.
J. Chem. Inf. Model., 2022

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.
CoRR, 2022

A Probabilistic Deep Image Prior for Computational Tomography.
CoRR, 2022

BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Action-Sufficient State Representation Learning for Control with Structural Constraints.
Proceedings of the International Conference on Machine Learning, 2022

Fast Relative Entropy Coding with A* coding.
Proceedings of the International Conference on Machine Learning, 2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.
Proceedings of the International Conference on Machine Learning, 2022

Invariant Causal Representation Learning for Out-of-Distribution Generalization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Resampling Base Distributions of Normalizing Flows.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Sampling the Variational Posterior with Local Refinement.
Entropy, 2021

Depth Uncertainty Networks for Active Learning.
CoRR, 2021

Bootstrap Your Flow.
CoRR, 2021

Contextual HyperNetworks for Novel Feature Adaptation.
CoRR, 2021

Nonlinear Invariant Risk Minimization: A Causal Approach.
CoRR, 2021

Improving black-box optimization in VAE latent space using decoder uncertainty.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Functional Variational Inference based on Stochastic Process Generators.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Deep Learning via Subnetwork Inference.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Active Slices for Sliced Stein Discrepancy.
Proceedings of the 38th International Conference on Machine Learning, 2021

Symmetry-Aware Actor-Critic for 3D Molecular Design.
Proceedings of the 9th International Conference on Learning Representations, 2021

Activation-level uncertainty in deep neural networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

Getting a CLUE: A Method for Explaining Uncertainty Estimates.
Proceedings of the 9th International Conference on Learning Representations, 2021

Sliced Kernelized Stein Discrepancy.
Proceedings of the 9th International Conference on Learning Representations, 2021

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not.
Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

Predictive Complexity Priors.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Educational Question Mining At Scale: Prediction, Analysis and Personalization.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Combining deep generative and discriminative models for Bayesian semi-supervised learning.
Pattern Recognit., 2020

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation.
CoRR, 2020

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks.
CoRR, 2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.
CoRR, 2020

Diagnostic Questions: The NeurIPS 2020 Education Challenge.
CoRR, 2020

DRIFT: Deep Reinforcement Learning for Functional Software Testing.
CoRR, 2020

Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures.
CoRR, 2020

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders.
CoRR, 2020

Variational Depth Search in ResNets.
CoRR, 2020

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Barking up the right tree: an approach to search over molecule synthesis DAGs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Depth Uncertainty in Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge.
Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs.
Proceedings of the ISLPED '20: ACM/IEEE International Symposium on Low Power Electronics and Design, 2020

Reinforcement Learning for Molecular Design Guided by Quantum Mechanics.
Proceedings of the 37th International Conference on Machine Learning, 2020

A Generative Model for Molecular Distance Geometry.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection.
CoRR, 2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model.
CoRR, 2019

'In-Between' Uncertainty in Bayesian Neural Networks.
CoRR, 2019

A COLD Approach to Generating Optimal Samples.
CoRR, 2019

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care.
CoRR, 2019

Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization.
IEEE Comput. Archit. Lett., 2019

Bayesian Batch Active Learning as Sparse Subset Approximation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Model to Search for Synthesizable Molecules.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dropout as a Structured Shrinkage Prior.
Proceedings of the 36th International Conference on Machine Learning, 2019

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE.
Proceedings of the 36th International Conference on Machine Learning, 2019

Variational Implicit Processes.
Proceedings of the 36th International Conference on Machine Learning, 2019

Deterministic Variational Inference for Robust Bayesian Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters.
Proceedings of the 7th International Conference on Learning Representations, 2019

Meta-Learning For Stochastic Gradient MCMC.
Proceedings of the 7th International Conference on Learning Representations, 2019

Generating Molecules via Chemical Reactions.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

A Generative Model For Electron Paths.
Proceedings of the 7th International Conference on Learning Representations, 2019

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018
Deconfounding Reinforcement Learning in Observational Settings.
CoRR, 2018

Successor Uncertainties: exploration and uncertainty in temporal difference learning.
CoRR, 2018

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks.
CoRR, 2018

Predicting Electron Paths.
CoRR, 2018

Variational Measure Preserving Flows.
CoRR, 2018

Taking Gradients Through Experiments: LSTMs and Memory Proximal Policy Optimization for Black-Box Quantum Control.
Proceedings of the High Performance Computing, 2018

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning a Generative Model for Validity in Complex Discrete Structures.
Proceedings of the 6th International Conference on Learning Representations, 2018

Sensitivity analysis for predictive uncertainty.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems.
CoRR, 2017

Actively Learning what makes a Discrete Sequence Valid.
CoRR, 2017

A case for efficient accelerator design space exploration via Bayesian optimization.
Proceedings of the 2017 IEEE/ACM International Symposium on Low Power Electronics and Design, 2017

Grammar Variational Autoencoder.
Proceedings of the 34th International Conference on Machine Learning, 2017

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control.
Proceedings of the 34th International Conference on Machine Learning, 2017

Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space.
Proceedings of the 34th International Conference on Machine Learning, 2017

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
A General Framework for Constrained Bayesian Optimization using Information-based Search.
J. Mach. Learn. Res., 2016

GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution.
CoRR, 2016

Automatic chemical design using a data-driven continuous representation of molecules.
CoRR, 2016

Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators.
Proceedings of the 43rd ACM/IEEE Annual International Symposium on Computer Architecture, 2016

Black-Box Alpha Divergence Minimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Predictive Entropy Search for Multi-objective Bayesian Optimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Scalable Gaussian Process Classification via Expectation Propagation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Expectation propagation in linear regression models with spike-and-slab priors.
Mach. Learn., 2015

Stochastic Expectation Propagation.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.
Proceedings of the 32nd International Conference on Machine Learning, 2015

A Probabilistic Model for Dirty Multi-task Feature Selection.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Gaussian Process Volatility Model.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Cold-start Active Learning with Robust Ordinal Matrix Factorization.
Proceedings of the 31th International Conference on Machine Learning, 2014

Probabilistic Matrix Factorization with Non-random Missing Data.
Proceedings of the 31th International Conference on Machine Learning, 2014

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation.
J. Mach. Learn. Res., 2013

Gaussian Process Conditional Copulas with Applications to Financial Time Series.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning Feature Selection Dependencies in Multi-task Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Dynamic Covariance Models for Multivariate Financial Time Series.
Proceedings of the 30th International Conference on Machine Learning, 2013

Gaussian Process Vine Copulas for Multivariate Dependence.
Proceedings of the 30th International Conference on Machine Learning, 2013

Bringing Representativeness into Social Media Monitoring and Analysis.
Proceedings of the 46th Hawaii International Conference on System Sciences, 2013

2012
Semi-Supervised Domain Adaptation with Non-Parametric Copulas.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Collaborative Gaussian Processes for Preference Learning.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Network-based sparse Bayesian classification.
Pattern Recognit., 2011

Semiparametric bivariate Archimedean copulas.
Comput. Stat. Data Anal., 2011

Robust Multi-Class Gaussian Process Classification.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Gaussianity Measures for Detecting the Direction of Causal Time Series.
Proceedings of the IJCAI 2011, 2011

2010
Expectation Propagation for microarray data classification.
Pattern Recognit. Lett., 2010

Expectation Propagation for Bayesian Multi-task Feature Selection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Hub Gene Selection Methods for the Reconstruction of Transcription Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

2008
Bayes Machines for binary classification.
Pattern Recognit. Lett., 2008

2007
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

GARCH Processes with Non-parametric Innovations for Market Risk Estimation.
Proceedings of the Artificial Neural Networks, 2007

2006
Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm.
Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis.
Proceedings of the Artificial Neural Networks, 2006


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