Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis. - José Miguel Hernández-Lobato; Alberto Suárez 001 (ICANN (2), 2006)

Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Rubén Ruiz-Torrubiano; Ángel Valle (IDEAL, 2006)

GARCH Processes with Non-parametric Innovations for Market Risk Estimation. - José Miguel Hernández-Lobato; Daniel Hernández-Lobato; Alberto Suárez 001 (ICANN (2), 2007)

Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. - José Miguel Hernández-Lobato; Tjeerd Dijkstra; Tom Heskes (NIPS, 2007)

Bayes Machines for binary classification. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato (Pattern Recognit. Lett., 2008)

Hub Gene Selection Methods for the Reconstruction of Transcription Networks. - José Miguel Hernández-Lobato; Tjeerd Dijkstra (ECML/PKDD (1), 2010)

Expectation Propagation for Bayesian Multi-task Feature Selection. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Thibault Helleputte; Pierre Dupont (ECML/PKDD (1), 2010)

Expectation Propagation for microarray data classification. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Alberto Suárez 001 (Pattern Recognit. Lett., 2010)

Gaussianity Measures for Detecting the Direction of Causal Time Series. - José Miguel Hernández-Lobato; Pablo Morales-Mombiela; Alberto Suárez 001 (IJCAI, 2011)

Robust Multi-Class Gaussian Process Classification. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Pierre Dupont (NIPS, 2011)

Semiparametric bivariate Archimedean copulas. - José Miguel Hernández-Lobato; Alberto Suárez 001 (Comput. Stat. Data Anal., 2011)

Network-based sparse Bayesian classification. - José Miguel Hernández-Lobato; Daniel Hernández-Lobato; Alberto Suárez 001 (Pattern Recognit., 2011)

Collaborative Gaussian Processes for Preference Learning. - Neil Houlsby; José Miguel Hernández-Lobato; Ferenc Huszar; Zoubin Ghahramani (NIPS, 2012)

Semi-Supervised Domain Adaptation with Non-Parametric Copulas. - David López-Paz; José Miguel Hernández-Lobato; Bernhard Schölkopf (NIPS, 2012)

Bringing Representativeness into Social Media Monitoring and Analysis. - Michael Kaschesky; Pawel Sobkowicz; José Miguel Hernández-Lobato; Guillaume Bouchard; Cédric Archambeau; Nicolas Scharioth; Robert Manchin; Adrian Gschwend; Reinhard Riedl (HICSS, 2013)

Gaussian Process Vine Copulas for Multivariate Dependence. - David Lopez-Paz; José Miguel Hernández-Lobato; Zoubin Ghahramani (ICML (2), 2013)

Dynamic Covariance Models for Multivariate Financial Time Series. - Yue Wu; José Miguel Hernández-Lobato; Zoubin Ghahramani (ICML (3), 2013)

Learning Feature Selection Dependencies in Multi-task Learning. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato (NIPS, 2013)

Gaussian Process Conditional Copulas with Applications to Financial Time Series. - José Miguel Hernández-Lobato; James Robert Lloyd; Daniel Hernández-Lobato (NIPS, 2013)

Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Pierre Dupont (J. Mach. Learn. Res., 2013)

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. - José Miguel Hernández-Lobato; Neil Houlsby; Zoubin Ghahramani (ICML, 2014)

Probabilistic Matrix Factorization with Non-random Missing Data. - José Miguel Hernández-Lobato; Neil Houlsby; Zoubin Ghahramani (ICML, 2014)

Cold-start Active Learning with Robust Ordinal Matrix Factorization. - Neil Houlsby; José Miguel Hernández-Lobato; Zoubin Ghahramani (ICML, 2014)

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. - José Miguel Hernández-Lobato; Matthew W. Hoffman; Zoubin Ghahramani (NIPS, 2014)

Gaussian Process Volatility Model. - Yue Wu; José Miguel Hernández-Lobato; Zoubin Ghahramani (NIPS, 2014)

A Probabilistic Model for Dirty Multi-task Feature Selection. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Zoubin Ghahramani (ICML, 2015)

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. - José Miguel Hernández-Lobato; Michael A. Gelbart; Matthew W. Hoffman; Ryan P. Adams; Zoubin Ghahramani (ICML, 2015)

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. - José Miguel Hernández-Lobato; Ryan P. Adams (ICML, 2015)

Stochastic Expectation Propagation. - Yingzhen Li; José Miguel Hernández-Lobato; Richard E. Turner (NIPS, 2015)

Expectation propagation in linear regression models with spike-and-slab priors. - José Miguel Hernández-Lobato; Daniel Hernández-Lobato; Alberto Suárez 001 (Mach. Learn., 2015)

Scalable Gaussian Process Classification via Expectation Propagation. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato (AISTATS, 2016)

Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. - Viktoriia Sharmanska; Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Novi Quadrianto (CVPR, 2016)

Deep Gaussian Processes for Regression using Approximate Expectation Propagation. - Thang D. Bui; Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Yingzhen Li; Richard E. Turner (ICML, 2016)

Predictive Entropy Search for Multi-objective Bayesian Optimization. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Amar Shah 001; Ryan P. Adams (ICML, 2016)

Black-Box Alpha Divergence Minimization. - José Miguel Hernández-Lobato; Yingzhen Li; Mark Rowland 001; Thang D. Bui; Daniel Hernández-Lobato; Richard E. Turner (ICML, 2016)

Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. - Brandon Reagen; Paul N. Whatmough; Robert Adolf; Saketh Rama; Hyunkwang Lee; Sae Kyu Lee; José Miguel Hernández-Lobato; Gu-Yeon Wei; David M. Brooks (ISCA, 2016)

Automatic chemical design using a data-driven continuous representation of molecules. - Rafael Gómez-Bombarelli; David Duvenaud; José Miguel Hernández-Lobato; Jorge Aguilera-Iparraguirre; Timothy D. Hirzel; Ryan P. Adams; Alán Aspuru-Guzik (CoRR, 2016)

GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. - Matt J. Kusner; José Miguel Hernández-Lobato (CoRR, 2016)

A General Framework for Constrained Bayesian Optimization using Information-based Search. - José Miguel Hernández-Lobato; Michael A. Gelbart; Ryan P. Adams; Matthew W. Hoffman; Zoubin Ghahramani (J. Mach. Learn. Res., 2016)

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (ICLR (Poster), 2017)

Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. - José Miguel Hernández-Lobato; James Requeima; Edward O. Pyzer-Knapp; Alán Aspuru-Guzik (ICML, 2017)

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. - Natasha Jaques; Shixiang Gu; Dzmitry Bahdanau; José Miguel Hernández-Lobato; Richard E. Turner; Douglas Eck (ICML, 2017)

Grammar Variational Autoencoder. - Matt J. Kusner; Brooks Paige; José Miguel Hernández-Lobato (ICML, 2017)

A case for efficient accelerator design space exploration via Bayesian optimization. - Brandon Reagen; José Miguel Hernández-Lobato; Robert Adolf; Michael A. Gelbart; Paul N. Whatmough; Gu-Yeon Wei; David M. Brooks (ISLPED, 2017)

Actively Learning what makes a Discrete Sequence Valid. - David Janz; Jos van der Westhuizen; José Miguel Hernández-Lobato (CoRR, 2017)

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (CoRR, 2017)

Sensitivity analysis for predictive uncertainty. - Stefan Depeweg; José Miguel Hernández-Lobato; Steffen Udluft; Thomas A. Runkler (ESANN, 2018)

Learning a Generative Model for Validity in Complex Discrete Structures. - David Janz; Jos van der Westhuizen; Brooks Paige; Matt J. Kusner; José Miguel Hernández-Lobato (ICLR (Poster), 2018)

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. - Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft (ICML, 2018)

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. - Marton Havasi; José Miguel Hernández-Lobato; Juan José Murillo-Fuentes (NeurIPS, 2018)

Taking Gradients Through Experiments: LSTMs and Memory Proximal Policy Optimization for Black-Box Quantum Control. - Moritz August; José Miguel Hernández-Lobato (ISC Workshops, 2018)

Variational Measure Preserving Flows. - Yichuan Zhang; José Miguel Hernández-Lobato; Zoubin Ghahramani (CoRR, 2018)

Predicting Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (CoRR, 2018)

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. - Anqi Wu; Sebastian Nowozin; Edward Meeds; Richard E. Turner; José Miguel Hernández-Lobato; Alexander L. Gaunt (CoRR, 2018)

Successor Uncertainties: exploration and uncertainty in temporal difference learning. - David Janz; Jiri Hron; José Miguel Hernández-Lobato; Katja Hofmann; Sebastian Tschiatschek (CoRR, 2018)

Deconfounding Reinforcement Learning in Observational Settings. - Chaochao Lu; Bernhard Schölkopf; José Miguel Hernández-Lobato (CoRR, 2018)

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. - Chao Ma 019; Sebastian Tschiatschek; Yingzhen Li; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (AABI, 2019)

A Generative Model For Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Generating Molecules via Chemical Reactions. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (DGS@ICLR, 2019)

Meta-Learning For Stochastic Gradient MCMC. - Wenbo Gong 001; Yingzhen Li; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. - Marton Havasi; Robert Peharz; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Deterministic Variational Inference for Robust Bayesian Neural Networks. - Anqi Wu; Sebastian Nowozin; Edward Meeds; Richard E. Turner; José Miguel Hernández-Lobato; Alexander L. Gaunt (ICLR, 2019)

Variational Implicit Processes. - Chao Ma 019; Yingzhen Li; José Miguel Hernández-Lobato (ICML, 2019)

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. - Chao Ma 019; Sebastian Tschiatschek; Konstantina Palla; José Miguel Hernández-Lobato; Sebastian Nowozin; Cheng Zhang 005 (ICML, 2019)

Dropout as a Structured Shrinkage Prior. - Eric T. Nalisnick; José Miguel Hernández-Lobato; Padhraic Smyth (ICML, 2019)

A Model to Search for Synthesizable Molecules. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2019)

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Sebastian Nowozin; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2019)

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. - David Janz; Jiri Hron; Przemyslaw Mazur; Katja Hofmann; José Miguel Hernández-Lobato; Sebastian Tschiatschek (NeurIPS, 2019)

Bayesian Batch Active Learning as Sparse Subset Approximation. - Robert Pinsler; Jonathan Gordon 003; Eric T. Nalisnick; José Miguel Hernández-Lobato (NeurIPS, 2019)

Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization. - Kshitij Bhardwaj; Marton Havasi; Yuan Yao 006; David M. Brooks; José Miguel Hernández-Lobato; Gu-Yeon Wei (IEEE Comput. Archit. Lett., 2019)

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. - Anna-Lena Popkes; Hiske Overweg; Ari Ercole; Yingzhen Li; José Miguel Hernández-Lobato; Yordan Zaykov; Cheng Zhang 005 (CoRR, 2019)

A COLD Approach to Generating Optimal Samples. - Omar Mahmood; José Miguel Hernández-Lobato (CoRR, 2019)

'In-Between' Uncertainty in Bayesian Neural Networks. - Andrew Y. K. Foong; Yingzhen Li; José Miguel Hernández-Lobato; Richard E. Turner (CoRR, 2019)

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Richard E. Turner; Sebastian Nowozin; José Miguel Hernández-Lobato; Cheng Zhang 005 (CoRR, 2019)

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. - Erik A. Daxberger; José Miguel Hernández-Lobato (CoRR, 2019)

A Generative Model for Molecular Distance Geometry. - Gregor N. C. Simm; José Miguel Hernández-Lobato (ICML, 2020)

Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. - Gregor N. C. Simm; Robert Pinsler; José Miguel Hernández-Lobato (ICML, 2020)

A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs. - Kshitij Bhardwaj; Marton Havasi; Yuan Yao 006; David M. Brooks; José Miguel Hernández-Lobato; Gu-Yeon Wei (ISLPED, 2020)

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. - Zichao Wang 001; Angus Lamb; Evgeny Saveliev; Pashmina Cameron; Yordan Zaykov; José Miguel Hernández-Lobato; Richard E. Turner; Richard G. Baraniuk; Craig Barton; Simon Peyton Jones; Simon Woodhead 002; Cheng Zhang 005 (NeurIPS (Competition and Demos), 2020)

Depth Uncertainty in Neural Networks. - Javier Antorán; James Urquhart Allingham; José Miguel Hernández-Lobato (NeurIPS, 2020)

Barking up the right tree: an approach to search over molecule synthesis DAGs. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2020)

Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. - Gergely Flamich; Marton Havasi; José Miguel Hernández-Lobato (NeurIPS, 2020)

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. - Chao Ma 019; Sebastian Tschiatschek; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2020)

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. - Austin Tripp; Erik A. Daxberger; José Miguel Hernández-Lobato (NeurIPS, 2020)

Variational Depth Search in ResNets. - Javier Antorán; James Urquhart Allingham; José Miguel Hernández-Lobato (CoRR, 2020)

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. - Zichao Wang 001; Sebastian Tschiatschek; Simon Woodhead 002; José Miguel Hernández-Lobato; Simon Peyton Jones; Cheng Zhang 005 (CoRR, 2020)

Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures. - Alonso Marco; Alexander von Rohr; Dominik Baumann; José Miguel Hernández-Lobato; Sebastian Trimpe (CoRR, 2020)

DRIFT: Deep Reinforcement Learning for Functional Software Testing. - Luke Harries; Rebekah Storan Clarke; Timothy Chapman; Swamy V. P. L. N. Nallamalli; Levent Özgür; Shuktika Jain; Alex Leung; Steve Lim; Aaron Dietrich; José Miguel Hernández-Lobato; Tom Ellis; Cheng Zhang 005; et al. (CoRR, 2020)

Diagnostic Questions: The NeurIPS 2020 Education Challenge. - Zichao Wang 001; Angus Lamb; Evgeny Saveliev; Pashmina Cameron; Yordan Zaykov; José Miguel Hernández-Lobato; Richard E. Turner; Richard G. Baraniuk; Craig Barton; Simon Peyton Jones; Simon Woodhead 002; Cheng Zhang 005 (CoRR, 2020)

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2020)

FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks. - Weijie He; Xiaohao Mao; Chao Ma 019; José Miguel Hernández-Lobato; Ting Chen 006 (CoRR, 2020)

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. - Chaochao Lu; Biwei Huang; Ke Wang; José Miguel Hernández-Lobato; Kun Zhang 001; Bernhard Schölkopf (CoRR, 2020)

Combining deep generative and discriminative models for Bayesian semi-supervised learning. - Jonathan Gordon 003; José Miguel Hernández-Lobato (Pattern Recognit., 2020)

Educational Question Mining At Scale: Prediction, Analysis and Personalization. - Zichao Wang 001; Sebastian Tschiatschek; Simon Woodhead 002; José Miguel Hernández-Lobato; Simon Peyton Jones; Richard G. Baraniuk; Cheng Zhang 005 (AAAI, 2021)

Predictive Complexity Priors. - Eric T. Nalisnick; Jonathan Gordon 003; José Miguel Hernández-Lobato (AISTATS, 2021)

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. - Chelsea Murray; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (ICBINB@NeurIPS, 2021)

Sliced Kernelized Stein Discrepancy. - Wenbo Gong 001; Yingzhen Li; José Miguel Hernández-Lobato (ICLR, 2021)

Getting a CLUE: A Method for Explaining Uncertainty Estimates. - Javier Antorán; Umang Bhatt; Tameem Adel; Adrian Weller; José Miguel Hernández-Lobato (ICLR, 2021)

Activation-level uncertainty in deep neural networks. - Pablo Morales-Alvarez; Daniel Hernández-Lobato; Rafael Molina 001; José Miguel Hernández-Lobato (ICLR, 2021)

Symmetry-Aware Actor-Critic for 3D Molecular Design. - Gregor N. C. Simm; Robert Pinsler; Gábor Csányi; José Miguel Hernández-Lobato (ICLR, 2021)

Active Slices for Sliced Stein Discrepancy. - Wenbo Gong 001; Kaibo Zhang; Yingzhen Li; José Miguel Hernández-Lobato (ICML, 2021)

A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. - Andrew Campbell; Wenlong Chen; Vincent Stimper; José Miguel Hernández-Lobato; Yichuan Zhang (ICML, 2021)

Bayesian Deep Learning via Subnetwork Inference. - Erik A. Daxberger; Eric T. Nalisnick; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (ICML, 2021)

Functional Variational Inference based on Stochastic Process Generators. - Chao Ma 019; José Miguel Hernández-Lobato (NeurIPS, 2021)

Improving black-box optimization in VAE latent space using decoder uncertainty. - Pascal Notin; José Miguel Hernández-Lobato; Yarin Gal (NeurIPS, 2021)

Nonlinear Invariant Risk Minimization: A Causal Approach. - Chaochao Lu; Yuhuai Wu; José Miguel Hernández-Lobato; Bernhard Schölkopf (CoRR, 2021)

Contextual HyperNetworks for Novel Feature Adaptation. - Angus Lamb; Evgeny Saveliev; Yingzhen Li; Sebastian Tschiatschek; Camilla Longden; Simon Woodhead 002; José Miguel Hernández-Lobato; Richard E. Turner; Pashmina Cameron; Cheng Zhang 005 (CoRR, 2021)

Bootstrap Your Flow. - Laurence Illing Midgley; Vincent Stimper; Gregor N. C. Simm; José Miguel Hernández-Lobato (CoRR, 2021)

Depth Uncertainty Networks for Active Learning. - Chelsea Murray; James Urquhart Allingham; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2021)

Sampling the Variational Posterior with Local Refinement. - Marton Havasi; Jasper Snoek; Dustin Tran; Jonathan Gordon 003; José Miguel Hernández-Lobato (Entropy, 2021)

Resampling Base Distributions of Normalizing Flows. - Vincent Stimper; Bernhard Schölkopf; José Miguel Hernández-Lobato (AISTATS, 2022)

Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. - Ross M. Clarke; Elre Talea Oldewage; José Miguel Hernández-Lobato (ICLR, 2022)

Invariant Causal Representation Learning for Out-of-Distribution Generalization. - Chaochao Lu; Yuhuai Wu; José Miguel Hernández-Lobato; Bernhard Schölkopf (ICLR, 2022)

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. - Javier Antorán; David Janz; James Urquhart Allingham; Erik A. Daxberger; Riccardo Barbano; Eric T. Nalisnick; José Miguel Hernández-Lobato (ICML, 2022)

Fast Relative Entropy Coding with A* coding. - Gergely Flamich; Stratis Markou; José Miguel Hernández-Lobato (ICML, 2022)

Action-Sufficient State Representation Learning for Control with Structural Constraints. - Biwei Huang; Chaochao Lu; Liu Leqi; José Miguel Hernández-Lobato; Clark Glymour; Bernhard Schölkopf; Kun Zhang 001 (ICML, 2022)

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. - Ignacio Peis; Chao Ma 019; José Miguel Hernández-Lobato (NeurIPS, 2022)

BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis. - Weijie He; Xiaohao Mao; Chao Ma 019; Yu Huang; José Miguel Hernández-Lobato; Ting Chen 006 (WWW, 2022)

A Probabilistic Deep Image Prior for Computational Tomography. - Javier Antorán; Riccardo Barbano; Johannes Leuschner; José Miguel Hernández-Lobato; Bangti Jin (CoRR, 2022)

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. - Riccardo Barbano; Johannes Leuschner; Javier Antorán; Bangti Jin; José Miguel Hernández-Lobato (CoRR, 2022)

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. - Miguel García-Ortegón; Gregor N. C. Simm; Austin J. Tripp; José Miguel Hernández-Lobato; Andreas Bender 002; Sergio Bacallado (J. Chem. Inf. Model., 2022)

Sampling-based inference for large linear models, with application to linearised Laplace. - Javier Antorán; Shreyas Padhy; Riccardo Barbano; Eric T. Nalisnick; David Janz; José Miguel Hernández-Lobato (ICLR, 2023)

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. - Wenlin Chen; Austin Tripp; José Miguel Hernández-Lobato (ICLR, 2023)

Flow Annealed Importance Sampling Bootstrap. - Laurence Illing Midgley; Vincent Stimper; Gregor N. C. Simm; Bernhard Schölkopf; José Miguel Hernández-Lobato (ICLR, 2023)

Faster Relative Entropy Coding with Greedy Rejection Coding. - Gergely Flamich; Stratis Markou; José Miguel Hernández-Lobato (NeurIPS, 2023)

Compression with Bayesian Implicit Neural Representations. - Zongyu Guo; Gergely Flamich; Jiajun He 003; Zhibo Chen 001; José Miguel Hernández-Lobato (NeurIPS, 2023)

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. - Jihao Andreas Lin; Javier Antorán; Shreyas Padhy; David Janz; José Miguel Hernández-Lobato; Alexander Terenin (NeurIPS, 2023)

SE(3) Equivariant Augmented Coupling Flows. - Laurence I. Midgley; Vincent Stimper; Javier Antorán; Emile Mathieu; Bernhard Schölkopf; José Miguel Hernández-Lobato (NeurIPS, 2023)

Tanimoto Random Features for Scalable Molecular Machine Learning. - Austin Tripp; Sergio Bacallado; Sukriti Singh; José Miguel Hernández-Lobato (NeurIPS, 2023)

Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. - Riccardo Barbano; Javier Antorán; Johannes Leuschner; José Miguel Hernández-Lobato; Zeljko Kereta; Bangti Jin (CoRR, 2023)

Online Laplace Model Selection Revisited. - Jihao Andreas Lin; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2023)

Minimal Random Code Learning with Mean-KL Parameterization. - Jihao Andreas Lin; Gergely Flamich; José Miguel Hernández-Lobato (CoRR, 2023)

Graph Neural Stochastic Differential Equations. - Richard Bergna; Felix L. Opolka; Pietro Liò; José Miguel Hernández-Lobato (CoRR, 2023)

Genetic algorithms are strong baselines for molecule generation. - Austin Tripp; José Miguel Hernández-Lobato (CoRR, 2023)

Adam through a Second-Order Lens. - Ross M. Clarke; Baiyu Su; José Miguel Hernández-Lobato (CoRR, 2023)

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. - Szilvia Ujváry; Gergely Flamich; Vincent Fortuin; José Miguel Hernández-Lobato (CoRR, 2023)

Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. - Javier Antorán; Riccardo Barbano; Johannes Leuschner; José Miguel Hernández-Lobato; Bangti Jin (Trans. Mach. Learn. Res., 2023)

Improving Continual Learning by Accurate Gradient Reconstructions of the Past. - Erik A. Daxberger; Siddharth Swaroop; Kazuki Osawa; Rio Yokota; Richard E. Turner; José Miguel Hernández-Lobato; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

normflows: A PyTorch Package for Normalizing Flows. - Vincent Stimper; David Liu 005; Andrew Campbell; Vincent Berenz; Lukas Ryll; Bernhard Schölkopf; José Miguel Hernández-Lobato (Zenodo, 2023)

normflows: A PyTorch Package for Normalizing Flows. - Vincent Stimper; David Liu 005; Andrew Campbell; Vincent Berenz; Lukas Ryll; Bernhard Schölkopf; José Miguel Hernández-Lobato (Zenodo, 2023)

normflows: A PyTorch Package for Normalizing Flows. - Vincent Stimper; David Liu 005; Andrew Campbell; Vincent Berenz; Lukas Ryll; Bernhard Schölkopf; José Miguel Hernández-Lobato (Zenodo, 2023)

normflows: A PyTorch Package for Normalizing Flows. - Vincent Stimper; David Liu 005; Andrew Campbell; Vincent Berenz; Lukas Ryll; Bernhard Schölkopf; José Miguel Hernández-Lobato (J. Open Source Softw., 2023)

normflows: A PyTorch Package for Normalizing Flows. - Vincent Stimper; David Liu 005; Andrew Campbell; Vincent Berenz; Lukas Ryll; Bernhard Schölkopf; José Miguel Hernández-Lobato (Zenodo, 2023)

RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. - Jiajun He 003; Gergely Flamich; Zongyu Guo; José Miguel Hernández-Lobato (ICLR, 2024)

Stochastic Gradient Descent for Gaussian Processes Done Right. - Jihao Andreas Lin; Shreyas Padhy; Javier Antorán; Austin Tripp; Alexander Terenin; Csaba Szepesvári; José Miguel Hernández-Lobato; David Janz (ICLR, 2024)

Retro-fallback: retrosynthetic planning in an uncertain world. - Austin Tripp; Krzysztof Maziarz; Sarah Lewis; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR, 2024)

Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. - Xuexin Chen; Ruichu Cai; Zhengting Huang; Yuxuan Zhu 001; Julien Horwood; Zhifeng Hao; Zijian Li 001; José Miguel Hernández-Lobato (ICML, 2024)

Diffusive Gibbs Sampling. - Wenlin Chen; Mingtian Zhang; Brooks Paige; José Miguel Hernández-Lobato; David Barber (ICML, 2024)

Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens. - Ross M. Clarke; José Miguel Hernández-Lobato (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Accelerating Relative Entropy Coding with Space Partitioning. - Jiajun He 003; Gergely Flamich; José Miguel Hernández-Lobato (NeurIPS, 2024)

A Generative Model of Symmetry Transformations. - James Urquhart Allingham; Bruno Mlodozeniec; Shreyas Padhy; Javier Antorán; David Krueger 001; Richard E. Turner; Eric T. Nalisnick; José Miguel Hernández-Lobato (NeurIPS, 2024)

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. - Jihao Andreas Lin; Shreyas Padhy; Bruno Mlodozeniec; Javier Antorán; José Miguel Hernández-Lobato (NeurIPS, 2024)

On conditional diffusion models for PDE simulations. - Aliaksandra Shysheya; Cristiana Diaconu; Federico Bergamin; Paris Perdikaris; José Miguel Hernández-Lobato; Richard E. Turner; Emile Mathieu (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. - Jihao Andreas Lin; Shreyas Padhy; Bruno Mlodozeniec; José Miguel Hernández-Lobato (CoRR, 2024)

Improving Antibody Design with Force-Guided Sampling in Diffusion Models. - Paulina Kulyte; Francisco Vargas 001; Simon Valentin Mathis; Yu Guang Wang 001; José Miguel Hernández-Lobato; Pietro Liò (CoRR, 2024)

Diagnosing and fixing common problems in Bayesian optimization for molecule design. - Austin Tripp; José Miguel Hernández-Lobato (CoRR, 2024)

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. - Fengzhe Zhang; Jiajun He 003; Laurence I. Midgley; Javier Antorán; José Miguel Hernández-Lobato (CoRR, 2024)

BEnDEM:A Boltzmann Sampler Based on Bootstrapped Denoising Energy Matching. - Ruikang Ouyang; Bo Qiang; José Miguel Hernández-Lobato (CoRR, 2024)

Getting Free Bits Back from Rotational Symmetries in LLMs. - Jiajun He 003; Gergely Flamich; José Miguel Hernández-Lobato (CoRR, 2024)

Batched Bayesian optimization with correlated candidate uncertainties. - Jenna C. Fromer; Runzhong Wang; Mrunali Manjrekar; Austin Tripp; José Miguel Hernández-Lobato; Connor W. Coley (CoRR, 2024)

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach. - Jingyi Zhao 002; Yuxuan Ou; Austin Tripp; Morteza Rasoulianboroujeni; José Miguel Hernández-Lobato (CoRR, 2024)

A Deep Generative Model for the Design of Synthesizable Ionizable Lipids. - Yuxuan Ou; Jingyi Zhao 002; Austin Tripp; Morteza Rasoulianboroujeni; José Miguel Hernández-Lobato (CoRR, 2024)

Image Reconstruction via Deep Image Prior Subspaces. - Riccardo Barbano; Javier Antorán; Johannes Leuschner; José Miguel Hernández-Lobato; Bangti Jin; Zeljko Kereta (Trans. Mach. Learn. Res., 2024)

Leveraging Task Structures for Improved Identifiability in Neural Network Representations. - Wenlin Chen; Julien Horwood; Juyeon Heo; José Miguel Hernández-Lobato (Trans. Mach. Learn. Res., 2024)

Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. - Elre Talea Oldewage; Ross M. Clarke; José Miguel Hernández-Lobato (Trans. Mach. Learn. Res., 2024)

Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. - Pablo Morales-Álvarez; Arne Schmidt 005; José Miguel Hernández-Lobato; Rafael Molina 001 (Pattern Recognit., 2024)

Training Neural Samplers with Reverse Diffusive KL Divergence. - Jiajun He 003; Wenlin Chen; Mingtian Zhang; David Barber; José Miguel Hernández-Lobato (AISTATS, 2025)

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. - Richard Bergna; Sergio Calvo-Ordoñez; Felix L. Opolka; Pietro Lio; José Miguel Hernández-Lobato (ICLR, 2025)

FedEDM: Federated Equivariant Diffusion Model for 3D Molecule Generation with Enhanced Communication Efficiency. - Zixing Song; Irwin King; José Miguel Hernández-Lobato (WWW (Companion Volume), 2025)

Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum. - Jenna C. Fromer; Runzhong Wang; Mrunali Manjrekar; Austin Tripp; José Miguel Hernández-Lobato; Connor W. Coley (J. Chem. Inf. Model., 2025)

Observation Noise and Initialization in Wide Neural Networks. - Sergio Calvo-Ordoñez; Jonathan Plenk; Richard Bergna; Alvaro Cartea; José Miguel Hernández-Lobato; Konstantina Palla; Kamil Ciosek (CoRR, 2025)

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Dinghuai Zhang; Shreyas Padhy; Ruikang Ouyang; Carla P. Gomes; José Miguel Hernández-Lobato (CoRR, 2025)

Towards Training One-Step Diffusion Models Without Distillation. - Mingtian Zhang; Jiajun He 003; Wenlin Chen; Zijing Ou; José Miguel Hernández-Lobato; Bernhard Schölkopf; David Barber (CoRR, 2025)

Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination. - Weilin Chen 001; Ruichu Cai; Junjie Wan; Zeqin Yang; José Miguel Hernández-Lobato (CoRR, 2025)

Long-term Causal Inference via Modeling Sequential Latent Confounding. - Weilin Chen 001; Ruichu Cai; Yuguang Yan; Zhifeng Hao; José Miguel Hernández-Lobato (CoRR, 2025)

Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption. - Wei Chen 103; Ruichu Cai; Jie Qiao; Yuguang Yan; José Miguel Hernández-Lobato (CoRR, 2025)

Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes. - Richard Bergna; Stefan Depeweg; Sergio Calvo-Ordoñez; Jonathan Plenk; Alvaro Cartea; José Miguel Hernández-Lobato (CoRR, 2025)

FEAT: Free energy Estimators with Adaptive Transport. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Yuanqing Wang; Carla P. Gomes; José Miguel Hernández-Lobato; Eric Vanden-Eijnden (CoRR, 2025)

Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models. - Fengzhe Zhang; Laurence I. Midgley; José Miguel Hernández-Lobato (CoRR, 2025)

Aligning Multimodal Representations through an Information Bottleneck. - Antonio Almudévar; José Miguel Hernández-Lobato; Sameer Khurana; Ricard Marxer; Alfonso Ortega 001 (CoRR, 2025)

There Was Never a Bottleneck in Concept Bottleneck Models. - Antonio Almudévar; José Miguel Hernández-Lobato; Alfonso Ortega 001 (CoRR, 2025)

Progressive Tempering Sampler with Diffusion. - Severi Rissanen; Ruikang Ouyang; Jiajun He 003; Wenlin Chen; Markus Heinonen; Arno Solin; José Miguel Hernández-Lobato (CoRR, 2025)

RNE: a plug-and-play framework for diffusion density estimation and inference-time control. - Jiajun He 003; José Miguel Hernández-Lobato; Yuanqi Du; Francisco Vargas 001 (CoRR, 2025)

Scalable Gaussian Processes with Latent Kronecker Structure. - Jihao Andreas Lin; Sebastian Ament; Maximilian Balandat; David Eriksson; José Miguel Hernández-Lobato; Eytan Bakshy (CoRR, 2025)

LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction. - Asal Mehradfar; Mohammad Shahab Sepehri; José Miguel Hernández-Lobato; Glen S. Kwon; Mahdi Soltanolkotabi; Salman Avestimehr; Morteza Rasoulianboroujeni (CoRR, 2025)

Weighted Conditional Flow Matching. - Sergio Calvo-Ordoñez; Matthieu Meunier; Alvaro Cartea; Christoph Reisinger; Yarin Gal; José Miguel Hernández-Lobato (CoRR, 2025)

Best practices for multi-fidelity Bayesian optimization in materials and molecular research. - Victor Sabanza Gil; Riccardo Barbano; Daniel Pacheco Gutiérrez; Jeremy S. Luterbacher; José Miguel Hernández-Lobato; Philippe Schwaller; Loïc Roch (Nat. Comput. Sci., 2025)