← Authors

José Miguel Hernández-Lobato

83 papers · 2013–2025 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (27) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (6) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (12) 🏠 Conference Loyalist (27) 🌟 Keyword Trendsetter Combo (5) 🤝 Dynamic Duo (10) 👑 Triple Crown 🔬 Deep Specialist (10) 🏆 Keyword Champion (2) 🏆 Grand Slam 👥 Mega-Team (25) 🗃️ Keyword Collector (94) 📈 Trend Setter 🔥 Unstoppable (13) ⚡ Prolific Year (11) 💎 Century Club (83) 🚀 Conference Pioneer

Conferences

ICML (30) NIPS (27) ICLR (18) AISTATS (4) JMLR (2) AAAI (1) CVPR (1)

Top co-authors

Javier Antoran (10) Zoubin Ghahramani (8) Eric Nalisnick (7) Daniel Hernández-lobato (7) Yingzhen Li (6) Brooks Paige (6) Gergely Flamich (6) Chao Ma (5) Bernhard Schölkopf (5) Jiajun He (5)

Keywords

bayesian inference (13) variational autoencoder (12) gaussian process (8) variational inference (8) expectation propagation (6) uncertainty quantification (6) bayesian optimization (5) black-box optimization (5) generative model (5) relative entropy coding (5) bayesian deep learning (4) reinforcement learning (4) deep generative model (4) importance sampling (3) predictive entropy search (3) probabilistic model (3) hyperparameter optimization (3) posterior approximation (3) molecular generation (3) probabilistic modeling (2)

Papers

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations ICLR 2025
Training Neural Samplers with Reverse Diffusive KL Divergence AISTATS 2025
Aligning Multimodal Representations through an Information Bottleneck ICML 2025
Scalable Gaussian Processes with Latent Kronecker Structure ICML 2025
Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space ICML 2025
Progressive Tempering Sampler with Diffusion ICML 2025
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation ICML 2024
On conditional diffusion models for PDE simulations NIPS 2024
A Generative Model of Symmetry Transformations NIPS 2024
Stochastic Gradient Descent for Gaussian Processes Done Right ICLR 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens ICML 2024
Diffusive Gibbs Sampling ICML 2024
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations ICLR 2024
Retro-fallback: retrosynthetic planning in an uncertain world ICLR 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes NIPS 2024
Accelerating Relative Entropy Coding with Space Partitioning NIPS 2024
Flow Annealed Importance Sampling Bootstrap ICLR 2023
Compression with Bayesian Implicit Neural Representations NIPS 2023
Tanimoto Random Features for Scalable Molecular Machine Learning NIPS 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent NIPS 2023
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction ICLR 2023
Sampling-based inference for large linear models, with application to linearised Laplace ICLR 2023
Faster Relative Entropy Coding with Greedy Rejection Coding NIPS 2023
SE(3) Equivariant Augmented Coupling Flows NIPS 2023
Invariant Causal Representation Learning for Out-of-Distribution Generalization ICLR 2022
Action-Sufficient State Representation Learning for Control with Structural Constraints ICML 2022
Fast Relative Entropy Coding with A* coding ICML 2022
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo NIPS 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning ICML 2022
Resampling Base Distributions of Normalizing Flows AISTATS 2022
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation ICLR 2022
Active Slices for Sliced Stein Discrepancy ICML 2021
Bayesian Deep Learning via Subnetwork Inference ICML 2021
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization ICML 2021
Educational Question Mining At Scale: Prediction, Analysis and Personalization AAAI 2021
Functional Variational Inference based on Stochastic Process Generators NIPS 2021
Improving black-box optimization in VAE latent space using decoder uncertainty NIPS 2021
Sliced Kernelized Stein Discrepancy ICLR 2021
Activation-level uncertainty in deep neural networks ICLR 2021
Symmetry-Aware Actor-Critic for 3D Molecular Design ICLR 2021
Getting a CLUE: A Method for Explaining Uncertainty Estimates ICLR 2021
Predictive Complexity Priors AISTATS 2021
Barking up the right tree: an approach to search over molecule synthesis DAGs NIPS 2020
Depth Uncertainty in Neural Networks NIPS 2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data NIPS 2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining NIPS 2020
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding NIPS 2020
A Generative Model for Molecular Distance Geometry ICML 2020
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics ICML 2020
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters ICLR 2019
Variational Implicit Processes ICML 2019
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE ICML 2019
Dropout as a Structured Shrinkage Prior ICML 2019
A Generative Model For Electron Paths ICLR 2019
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model NIPS 2019
Bayesian Batch Active Learning as Sparse Subset Approximation NIPS 2019
A Model to Search for Synthesizable Molecules NIPS 2019
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning NIPS 2019
Deterministic Variational Inference for Robust Bayesian Neural Networks ICLR 2019
Meta-Learning For Stochastic Gradient MCMC ICLR 2019
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo NIPS 2018
Learning a Generative Model for Validity in Complex Discrete Structures ICLR 2018
Grammar Variational Autoencoder ICML 2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space ICML 2017
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control ICML 2017
A General Framework for Constrained Bayesian Optimization using Information-based Search JMLR 2016
Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations CVPR 2016
Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016
Stochastic Expectation Propagation NIPS 2015
A Probabilistic Model for Dirty Multi-task Feature Selection ICML 2015
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints ICML 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions NIPS 2014
Gaussian Process Volatility Model NIPS 2014
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices ICML 2014
Cold-start Active Learning with Robust Ordinal Matrix Factorization ICML 2014
Probabilistic Matrix Factorization with Non-random Missing Data ICML 2014
Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation JMLR 2013
Gaussian Process Conditional Copulas with Applications to Financial Time Series NIPS 2013
Gaussian Process Vine Copulas for Multivariate Dependence ICML 2013
Dynamic Covariance Models for Multivariate Financial Time Series ICML 2013
Learning Feature Selection Dependencies in Multi-task Learning NIPS 2013