José Miguel Hernández-Lobato

University of Cambridge — United Kingdom

Computer Science · Physical Sciences

38h-index8.4kcitations253works1.22yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Gaussian Processes and Bayesian Inference(68), Machine Learning in Materials Science(36), Machine Learning and Algorithms(34), Machine Learning and Data Classification(32), Generative Adversarial Networks and Image Synthesis(28)

Publications253 total

Mitigating Forgetting in Low Rank Adaptation
arXiv (Cornell University)·2025Open Access
Mitigating Forgetting in Low Rank Adaptation
ArXiv.org·2025Open Access
Improving Iterative Gaussian Processes via Warm Starting Sequential Posteriors
arXiv (Cornell University)·2025Open Access
CREPE: Controlling Diffusion with Replica Exchange
arXiv (Cornell University)·2025Open Access
Bridging Innovation and Efficiency: The Promises and Challenges of Self-Driving Labs as Sustainable Drivers for Chemistry
CHIMIA International Journal for Chemistry·2025· 1 citedOpen Access
Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time
2025Open Access
Best practices for multi-fidelity Bayesian optimization in materials and molecular research
Nature Computational Science·2025· 9 citedOpen Access
LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction
ArXiv.org·2025· 1 citedOpen Access
Scalable Gaussian Processes with Latent Kronecker Structure
arXiv (Cornell University)·2025Open Access
RNE: plug-and-play diffusion inference-time control and energy-based training
arXiv (Cornell University)·2025Open Access
Aligning Multimodal Representations through an Information Bottleneck
arXiv (Cornell University)·2025Open Access
There Was Never a Bottleneck in Concept Bottleneck Models
arXiv (Cornell University)·2025Open Access
Progressive Tempering Sampler with Diffusion
arXiv (Cornell University)·2025Open Access
Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models
arXiv (Cornell University)·2025Open Access
FedEDM: Federated Equivariant Diffusion Model for 3D Molecule Generation with Enhanced Communication Efficiency
2025Open Access
Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum
Journal of Chemical Information and Modeling·2025· 5 cited
Bayesian Meta‐Learning for Few‐Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins
Angewandte Chemie·2025Open Access
Bayesian Meta‐Learning for Few‐Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins
Angewandte Chemie International Edition·2025Open Access
A meta-learning approach for selectivity prediction in asymmetric catalysis
Nature Communications·2025· 5 citedOpen Access
FEAT: Free energy Estimators with Adaptive Transport
arXiv (Cornell University)·2025Open Access
page 1 of 13Next →

Frequent Co-authors

Daniel Hernández-Lobato(10), Zoubin Ghahramani(8), Richard E. Turner(8), Yingzhen Li(6), Ryan P. Adams(5), Matt J. Kusner(4), Thang D. Bui(4), Stefan Depeweg(4), Finale Doshi‐Velez(4), Steffen Udluft(4), Alán Aspuru‐Guzik(3), Brandon Reagen(3), Robert Adolf(3), Gu-Yeon Wei(3), David Brooks(3), Matthew W. Hoffman(3), Michael A. Gelbart(3), Neil Houlsby(3), Jonathan Gordon(3), Paul N. Whatmough(2)