Hernández-Lobato, José Miguel

118 publications

ICML 2025 Aligning Multimodal Representations Through an Information Bottleneck Antonio Almudévar, José Miguel Hernández-Lobato, Sameer Khurana, Ricard Marxer, Alfonso Ortega

ICML 2025 Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space Zixing Song, Ziqiao Meng, José Miguel Hernández-Lobato

TMLR 2025 Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models Fengzhe Zhang, Laurence Illing Midgley, José Miguel Hernández-Lobato

NeurIPS 2025 FEAT: Free Energy Estimators with Adaptive Transport Yuanqi Du, Jiajun He, Francisco Vargas, Yuanqing Wang, Carla P Gomes, José Miguel Hernández-Lobato, Eric Vanden-Eijnden

ICLRW 2025 No Trick, No Treat: Pursuits and Challenges Towards Simulation-Free Training of Neural Samplers Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, RuiKang OuYang, Carla P Gomes, José Miguel Hernández-Lobato

ICML 2025 Progressive Tempering Sampler with Diffusion Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato

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

ICLRW 2025 Towards Training One-Step Diffusion Models Without Distillation Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber

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

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

NeurIPSW 2024 A Deep Generative Model for the Design of Synthesizable Ionizable Lipids Yuxuan Ou, Jingyi Zhao, Austin Tripp, Morteza Rasoulianboroujeni, José Miguel Hernández-Lobato

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

NeurIPS 2024 Accelerating Relative Entropy Coding with Space Partitioning Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato

NeurIPSW 2024 Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy Scott Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch

ICMLW 2024 Diagnosing and Fixing Common Problems in Bayesian Optimization for Molecule Design Austin Tripp, José Miguel Hernández-Lobato

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

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

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

NeurIPSW 2024 Getting Free Bits Back from Rotational Symmetries in LLMs Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato

ICLRW 2024 Guided Autoregressive Diffusion Models with Applications to PDE Simulation Federico Bergamin, Cristiana Diaconu, Aliaksandra Shysheya, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu

TMLR 2024 Image Reconstruction via Deep Image Prior Subspaces Riccardo Barbano, Javier Antoran, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta

NeurIPSW 2024 Improving Antibody Design with Force-Guided Sampling in Diffusion Models Paulina Kulytė, Francisco Vargas, Simon V Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Lio

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

TMLR 2024 Leveraging Task Structures for Improved Identifiability in Neural Network Representations Wenlin Chen, Julien Horwood, Juyeon Heo, 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

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 Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

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

ICLR 2024 Retro-Fallback: Retrosynthetic Planning in an Uncertain World Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin Segler, José Miguel Hernández-Lobato

TMLR 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

ICLR 2024 Stochastic Gradient Descent for Gaussian Processes Done Right Jihao Andreas Lin, Shreyas Padhy, Javier Antoran, Austin Tripp, Alexander Terenin, Csaba Szepesvari, José Miguel Hernández-Lobato, David Janz

ICML 2024 Studying K-FAC Heuristics by Viewing Adam Through a Second-Order Lens Ross M Clarke, José Miguel Hernández-Lobato

NeurIPSW 2024 Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations Richard Bergna, Sergio Calvo Ordoñez, Felix Opolka, Pietro Lio, José Miguel Hernández-Lobato

NeurIPSW 2023 Adam Through a Second-Order Lens Ross M Clarke, Baiyu Su, José Miguel Hernández-Lobato

NeurIPS 2023 Compression with Bayesian Implicit Neural Representations Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato

NeurIPSW 2023 Estimating Optimal PAC-Bayes Bounds with Hamiltonian Monte Carlo Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato

NeurIPS 2023 Faster Relative Entropy Coding with Greedy Rejection Coding Gergely Flamich, Stratis Markou, 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

TMLR 2023 Improving Continual Learning by Accurate Gradient Reconstructions of the past Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

ICLR 2023 Meta-Learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato

ICMLW 2023 Minimal Random Code Learning with Mean-KL Parameterization Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato

NeurIPSW 2023 Retro-Fallback: Retrosynthetic Planning in an Uncertain World Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin Segler, José Miguel Hernández-Lobato

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

NeurIPSW 2023 SE(3) Equivariant Augmented Coupling Flows Laurence Illing Midgley, Vincent Stimper, Javier Antoran, Emile Mathieu, Bernhard Schölkopf, 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

ICLR 2023 Sampling-Based Inference for Large Linear Models, with Application to Linearised Laplace Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato

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

TMLR 2023 Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antoran, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

ICML 2022 Action-Sufficient State Representation Learning for Control with Structural Constraints Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

ICLRW 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

ICML 2022 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato

ICLRW 2022 An Evaluation Framework for the Objective Functions of De Novo Drug Design Benchmarks Austin Tripp, Wenlin Chen, José Miguel Hernández-Lobato

ICML 2022 Fast Relative Entropy Coding with A* Coding Gergely Flamich, Stratis Markou, Jose Miguel Hernandez-Lobato

NeurIPSW 2022 Flow Annealed Importance Sampling Bootstrap Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato

ICLRW 2022 Invariant Causal Representation Learning for Generalization in Imitation and Reinforcement Learning Chaochao Lu, José Miguel Hernández-Lobato, Bernhard Schölkopf

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

NeurIPSW 2022 Learning Generative Models with Invariance to Symmetries James Urquhart Allingham, Javier Antoran, Shreyas Padhy, Eric Nalisnick, José Miguel Hernández-Lobato

NeurIPSW 2022 Meta-Learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato

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

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

NeurIPSW 2021 A Fresh Look at De Novo Molecular Design Benchmarks Austin Tripp, Gregor N. C. Simm, José Miguel Hernández-Lobato

ICML 2021 A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization Andrew Campbell, Wenlong Chen, Vincent Stimper, Jose Miguel Hernandez-Lobato, Yichuan Zhang

ICLR 2021 Activation-Level Uncertainty in Deep Neural Networks Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato

ICML 2021 Active Slices for Sliced Stein Discrepancy Wenbo Gong, Kaibo Zhang, Yingzhen Li, Jose Miguel Hernandez-Lobato

NeurIPSW 2021 Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not Chelsea Murray, James Urquhart Allingham, Javier Antoran, José Miguel Hernández-Lobato

ICML 2021 Bayesian Deep Learning via Subnetwork Inference Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antoran, Jose Miguel Hernandez-Lobato

AAAI 2021 Educational Question Mining at Scale: Prediction, Analysis and Personalization Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang

NeurIPS 2021 Functional Variational Inference Based on Stochastic Process Generators Chao Ma, José Miguel Hernández-Lobato

ICLR 2021 Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, 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

ICLR 2021 Sliced Kernelized Stein Discrepancy Wenbo Gong, Yingzhen Li, 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

ICML 2020 A Generative Model for Molecular Distance Geometry Gregor Simm, Jose Miguel Hernandez-Lobato

NeurIPS 2020 Barking up the Right Tree: An Approach to Search over Molecule Synthesis DAGs John Bradshaw, Brooks Paige, Matt J Kusner, Marwin 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 Depth Uncertainty in Neural Networks Javier Antoran, James Allingham, José Miguel Hernández-Lobato

ICML 2020 Reinforcement Learning for Molecular Design Guided by Quantum Mechanics Gregor Simm, Robert Pinsler, Jose Miguel Hernandez-Lobato

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

NeurIPS 2020 VAEM: A Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, Cheng Zhang

ICMLW 2020 VAEM: A Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

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

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

NeurIPS 2019 Bayesian Batch Active Learning as Sparse Subset Approximation Robert Pinsler, Jonathan Gordon, Eric Nalisnick, José Miguel Hernández-Lobato

ICLR 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

ICML 2019 Dropout as a Structured Shrinkage Prior Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth

ICML 2019 EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang

ICLRW 2019 Generating Molecules via Chemical Reactions John Bradshaw, Matt J. Kusner, Brooks Paige, 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, Sebastian Tschiatschek, Sebastian Nowozin, Richard E Turner, José Miguel Hernández-Lobato, Cheng Zhang

ICLR 2019 Meta-Learning for Stochastic Gradient MCMC Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

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

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

ICML 2019 Variational Implicit Processes Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato

ICML 2018 Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-Sensitive Learning Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, Steffen Udluft

NeurIPS 2018 Inference in Deep Gaussian Processes Using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes

ICLR 2018 Learning a Generative Model for Validity in Complex Discrete Structures Dave Janz, Jos van der Westhuizen, Brooks Paige, Matt Kusner, José Miguel Hernández-Lobato

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

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

ICML 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

JMLR 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

CVPR 2016 Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations Viktoriia Sharmanska, Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Novi Quadrianto

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

ICML 2015 A Probabilistic Model for Dirty Multi-Task Feature Selection Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

MLJ 2015 Expectation Propagation in Linear Regression Models with Spike-and-Slab Priors José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez

ICML 2015 Predictive Entropy Search for Bayesian Optimization with Unknown Constraints Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani

ICML 2015 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks Jose Miguel Hernandez-Lobato, Ryan Adams

NeurIPS 2015 Stochastic Expectation Propagation Yingzhen Li, José Miguel Hernández-Lobato, Richard E Turner

ICML 2014 Cold-Start Active Learning with Robust Ordinal Matrix Factorization Neil Houlsby, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

NeurIPS 2014 Gaussian Process Volatility Model Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani

NeurIPS 2014 Predictive Entropy Search for Efficient Global Optimization of Black-Box Functions José Miguel Hernández-Lobato, Matthew W Hoffman, Zoubin Ghahramani

ICML 2014 Probabilistic Matrix Factorization with Non-Random Missing Data Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani

ICML 2014 Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani

NeurIPS 2013 Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R Lloyd, Daniel Hernández-Lobato

ICML 2013 Gaussian Process Vine Copulas for Multivariate Dependence David Lopez-Paz, Jose Miguel Hernández-Lobato, Ghahramani Zoubin

JMLR 2013 Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont

NeurIPS 2013 Learning Feature Selection Dependencies in Multi-Task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato

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

ECML-PKDD 2010 Expectation Propagation for Bayesian Multi-Task Feature Selection Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont

ECML-PKDD 2010 Hub Gene Selection Methods for the Reconstruction of Transcription Networks José Miguel Hernández-Lobato, Tjeerd Dijkstra