José Miguel Hernández-Lobato
Professor of Machine Learning
Works on probabilistic machine learning, including Bayesian optimisation, approximate inference and machine learning for molecular design.
José Miguel Hernández-Lobato's lab at Cambridge works on probabilistic & causal inference and efficient training & inference. 60 papers since 2023, 43 at top venues, mostly ICML and NeurIPS. 6 PhD students. 28 alumni, most often to Microsoft and Isomorphic Labs. Applications close .
At a glance
- papers, 2023–2026
- 16/ year
- at T1 venues
- 72%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 28
Papers by yearfrom the lab homepage, 2023–2026
- 19 papers2026 · T1 15 · collecting
- 14 papers2025 · T1 9
- 16 papers2024 · T1 11
- 11 papers2023 · T1 8
T1 venuesOtherVenues: ICML, NeurIPS, ICLR, AISTATS, KDD
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference11 papers
- Efficient training & inference6 papers
- molecular design0 papers
- approximate inference0 papers
- bayesian optimisation0 papers
Paper list
From the lab homepage.
202619 papers
- ICML (T1)A Diffusive Classification Loss for Learning Energy-based Generative Models
- AISTATS (T1)A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks
- Transactions of Machine Learning ResearchBNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
- —Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
- ICML (T1)Conditional Diffusion Sampling
- ICLR (T1)CREPE: Controlling diffusion with REPlica Exchange
- NeurIPS (T1)DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
- ICML (T1)Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
- NeurIPS (T1)Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
- NeurIPS (T1)Free energy Estimation on Any State Space
- —Improving Molecular Property Prediction with Score-Based Diffusion Models
- —Machine-Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction
202514 papers
- Nature Communications (T1)A Meta-learning Approach for Selectivity Prediction in Asymmetric Catalysis
- ICML (T1)Aligning Multimodal Representations through an Information Bottleneck
- —Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum
- Angewandte ChemieBayesian Meta–Learning for Few–Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins
- Nature Computational ScienceBest Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
- 9Bridging Innovation and Efficiency: The Promises and Challenges of Self-Driving Labs as Sustainable Drivers for Chemistry
- ICML (T1)Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space
- Transactions of Machine Learning ResearchEfficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models
- NeurIPS (T1)FEAT: Free energy Estimators with Adaptive Transport
- ICML (T1)Progressive Tempering Sampling with Diffusion
- ICML (T1)Scalable Gaussian Processes with Latent Kronecker Structure
- KDD (T1)Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time
202416 papers
- NeurIPS (T1)A Generative Model of Symmetry Transformations
- NeurIPS (T1)Accelerating Relative Entropy Coding with Space Partitioning
- The Journal of Organic Chemistry , 89, 17, 12467-12478Data-Driven Insights on Transition Metal-Catalyzed Asymmetric Hydrogenation of Olefins
- Communications Chemistry, 7Deep Kernel Learning for Reaction Outcome Prediction and Optimization
- ICML (T1)Diffusive Gibbs Sampling
- ICML (T1)Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
- Transactions of Machine Learning ResearchImage Reconstruction via Deep Image Prior Subspaces
- NeurIPS (T1)Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
- Pattern RecognitionIntroducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images
- Transactions of Machine Learning ResearchLeveraging Task Structures for Improved Identifiability in Neural Network Representations
- NeurIPS (T1)On conditional diffusion models for PDE simulations
- ICML (T1)Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
202311 papers
- NeurIPS (T1)Compression with Bayesian Implicit Neural Representations
- NeurIPS (T1)Faster Relative Entropy Coding with Greedy Rejection Coding
- ICLR (T1)Flow Annealed Importance Sampling Bootstrap
- Transactions of Machine Learning ResearchImproving Continual Learning by Accurate Gradient Reconstructions of the Past
- ICLR (T1)Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
- Journal of Open Source Software, 8(86), 5361normflows: A PyTorch Package for Normalizing Flows
- NeurIPS (T1)Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
- ICLR (T1)Sampling-based inference for large linear models, with application to linearised Laplace
- NeurIPS (T1)SE(3) Equivariant Augmented Coupling Flows
- NeurIPS (T1)Tanimoto Random Features for Scalable Molecular Machine Learning
- Transactions of Machine Learning ResearchUncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
Where alumni went28 listed, 59% to industry
Industry 13Academia 9
Most often to
- Microsoft
- Isomorphic Labs
- Apple
Also to
- UCL
- Alberta Machine Intelligence Institute
- Bristol University
Apply to CambridgeMichaelmas 2027
PhD in Computer Science
- Round
- Funding round (8 Dec 2026)
- Opens
- GRE
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- English
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- Fee
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Confirmed on the program page.
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