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José Miguel Hernández-Lobato

José Miguel Hernández-Lobato

D-Index & Metrics

Computer Science

D-Index
52
Citations
12751
World Ranking
5028
National Ranking
302

Overview

José Miguel Hernández-Lobato is a researcher affiliated with the University of Cambridge in the United Kingdom. Their work predominantly spans the field of Computer Science, with a particular focus on Artificial Intelligence and its applications across several interdisciplinary areas.

Their main research areas include:

  • Machine Learning in Materials Science
  • Generative Adversarial Networks and Image Synthesis
  • Gaussian Processes and Bayesian Inference
  • Computational Drug Discovery Methods
  • Model Reduction and Neural Networks
  • Machine Learning and Data Classification
  • Adversarial Robustness in Machine Learning

Their notable recent publications exemplify the diversity of these interests. Key papers are:

  • DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design, 2022, Journal of Chemical Information and Modeling
  • Getting a CLUE: A Method for Explaining Uncertainty Estimates, 2020, arXiv (Cornell University)
  • Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining, 2020, arXiv (Cornell University)
  • normflows: A PyTorch Package for NormalizingFlows, 2023, The Journal of Open Source Software
  • Nonlinear Invariant Risk Minimization: A Causal Approach, 2021, arXiv (Cornell University)

Frequent co-authors in Hernández-Lobato's research include Javier Antorán, Austin Tripp, Bernhard Schölkopf, Jiajun He, and Vincent Stimper. Collaboration with these researchers has contributed to multiple projects spanning machine learning theory and applications.

The primary publication venues for their work reflect a strong presence in preprint archives and domain-specific journals. These venues include:

  • arXiv (Cornell University)
  • Apollo (University of Cambridge)
  • Journal of Chemical Information and Modeling
  • Scientific Repository (Petra Christian University)
  • Zenodo (CERN European Organization for Nuclear Research)

The researcher has contributed extensively to artificial intelligence research with a publication record highlighting 156 papers in computer science overall, with 89 specifically related to Artificial Intelligence and numerous others in related subfields such as Computer Vision and Materials Chemistry.

Best Publications

  • Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

    Rafael Gómez-Bombarelli;Jennifer Nansean Wei;David Duvenaud;José Miguel Hernández-Lobato

  • Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

    Jose Miguel Hernandez-Lobato;Ryan Adams

  • Grammar variational autoencoder

    Matt J. Kusner;Brooks Paige;José Miguel Hernández-Lobato

  • Minerva: enabling low-power, highly-accurate deep neural network accelerators

    Brandon Reagen;Paul Whatmough;Robert Adolf;Saketh Rama

  • Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

    José Miguel Hernández-Lobato;Matthew W Hoffman;Zoubin Ghahramani

  • Constrained Bayesian optimization for automatic chemical design using variational autoencoders.

    Ryan-Rhys Griffiths;José Miguel Hernández-Lobato;José Miguel Hernández-Lobato;José Miguel Hernández-Lobato

  • GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

    Matt J. Kusner;José Miguel Hernández-Lobato

  • Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

    Stefan Depeweg;José Miguel Hernández-Lobato;Finale Doshi-Velez;Steffen Udluft

  • Deep Gaussian processes for regression using approximate expectation propagation

    Thang D. Bui;José Miguel Hernández-Lobato;Daniel Hernández-Lobato;Yingzhen Li

  • Predictive entropy search for multi-objective Bayesian optimization

    Daniel Hernández-Lobato;José Miguel Hernández-Lobato;Amar Shah;Ryan P. Adams

  • Black-Box Alpha Divergence Minimization

    José Miguel Hernández-Lobato;Yingzhen Li;Mark Rowland;Thang D. Bui

  • Black-box α-divergence minimization

    José Miguel Hernández-Lobato;Yingzhen Li;Mark Rowland;Daniel Hernández-Lobato

  • Deep Gaussian Processes for Regression using Approximate Expectation Propagation

    Thang D. Bui;Daniel Hernández-Lobato;Yingzhen Li;José Miguel Hernández-Lobato

  • Probabilistic Matrix Factorization with Non-random Missing Data

    Jose Miguel Hernandez-Lobato;Neil Houlsby;Zoubin Ghahramani

  • A general framework for constrained Bayesian optimization using information-based search

    José Miguel Hernández-Lobato;Michael A. Gelbart;Ryan P. Adams;Matthew W. Hoffman

  • Collaborative Gaussian Processes for Preference Learning

    Neil Houlsby;Ferenc Huszar;Zoubin Ghahramani;Jose M. Hernández-lobato

  • Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

    Jose Miguel Hernandez-Lobato;Michael Gelbart;Matthew Hoffman;Ryan Adams

  • Learning and policy search in stochastic dynamical systems with Bayesian neural networks

    Stefan Depeweg;José Miguel Hernández-Lobato;Finale Doshi-Velez;Steffen Udluft

  • 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

  • Sequence tutor: conservative fine-tuning of sequence generation models with KL-control

    Natasha Jaques;Shixiang Gu;Dzmitry Bahdanau;José Miguel Hernández-Lobato

  • Deterministic Variational Inference for Robust Bayesian Neural Networks

    Anqi Wu;Sebastian Nowozin;Edward Meeds;Richard E. Turner

  • Stochastic expectation propagation

    Yingzhen Li;Jose Miguel Hernández-Lobato;Richard E. Turner

  • 'In-Between' Uncertainty in Bayesian Neural Networks

    Andrew Y. K. Foong;Yingzhen Li;José Miguel Hernández-Lobato;Richard E. Turner

Frequent Co-Authors

Richard E. Turner
Richard E. Turner University of Cambridge
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Ryan P. Adams
Ryan P. Adams Princeton University
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Simon Jones
Simon Jones Microsoft (United States)
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Alán Aspuru-Guzik
Alán Aspuru-Guzik University of Toronto
Finale Doshi-Velez
Finale Doshi-Velez Harvard University
David Lopez-Paz
David Lopez-Paz Facebook AI Research (FAIR) in Paris

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