World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
7091
World Ranking
5717
National Ranking
345

Overview

Richard E. Turner is affiliated with the University of Cambridge in the United Kingdom. Their primary field of study is Computer Science, with a notable focus on Artificial Intelligence. Other subfields include Atmospheric Science, Computer Vision and Pattern Recognition, Global and Planetary Change, and Statistical and Nonlinear Physics.

Their research covers multiple topics, prominently featuring Gaussian Processes and Bayesian Inference, Domain Adaptation and Few-Shot Learning, Machine Learning and Data Classification, Meteorological Phenomena and Simulations, Climate Variability and Models, Model Reduction and Neural Networks, and Adversarial Robustness in Machine Learning.

Richard E. Turner's publication record includes recent papers such as:

  • The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users (2021, Journal of the Association for Research in Otolaryngology)
  • Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (2020, Apollo (University of Cambridge))
  • Convolutional conditional neural processes for local climate downscaling (2022, Geoscientific Model Development)
  • A foundation model for the Earth system (2025, Nature)
  • Continual Deep Learning by Functional Regularisation of Memorable Past (2020, arXiv (Cornell University))

Frequent co-authors in their work include:

  • James Requeima
  • Wessel P. Bruinsma
  • J. Scott Hosking
  • Stratis Markou
  • Anna Vaughan

Richard E. Turner has published extensively in several venues, with arXiv (Cornell University) being the most frequent. Other prominent venues include Apollo (University of Cambridge), Nature, Environmental Data Science, and Hydrology and Earth System Sciences.

Best Publications

  • Variational continual learning

    Cuong V. Nguyen;Yingzhen Li;Thang D. Bui;Richard E. Turner

  • The processing and perception of size information in speech sounds

    David R. R. Smith;Roy D. Patterson;Richard Turner;Hideki Kawahara

  • Gaussian Process Behaviour in Wide Deep Neural Networks.

    Alexander G. de G. Matthews;Mark Rowland;Jiri Hron;Richard E. Turner

  • Two problems with variational expectation maximisation for time-series models

    Richard Eric Turner;Maneesh Sahani

  • Deep Gaussian processes for regression using approximate expectation propagation

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

  • Rényi divergence variational inference

    Yingzhen Li;Richard E. Turner

  • Invariant models for causal transfer learning

    Mateo Rojas-Carulla;Bernhard Schölkopf;Richard Turner;Jonas Peters

  • Q-PrOP: Sample-efficient policy gradient with an off-policy critic

    Shixiang Gu;Timothy Lillicrap;Zoubin Ghahramani;Richard Eric Turner

  • 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

  • Meta-Learning Probabilistic Inference for Prediction

    Jonathan Gordon;John Bronskill;Matthias Bauer;Sebastian Nowozin

  • Practical Deep Learning with Bayesian Principles

    Kazuki Osawa;Siddharth Swaroop;Mohammad Emtiyaz E. Khan;Anirudh Jain

  • R'enyi Divergence Variational Inference

    Yingzhen Li;Richard E. Turner

  • Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

    Shixiang Gu;Timothy P. Lillicrap;Zoubin Ghahramani;Richard E. Turner

  • A Maximum-Likelihood Interpretation for Slow Feature Analysis

    Richard Turner;Maneesh Sahani

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

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

  • A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

    Thang D. Bui;Josiah Yan;Richard E. Turner

  • Deterministic Variational Inference for Robust Bayesian Neural Networks

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

  • On sparse variational methods and the Kullback-Leibler divergence between stochastic processes

    Alexander G. de G. Matthews;James Hensman;Richard E. Turner;Zoubin Ghahramani

  • Neural adaptive sequential Monte Carlo

    Shixiang Gu;Zoubin Ghahramani;Richard E. Turner

  • Structured Evolution with Compact Architectures for Scalable Policy Optimization

    Krzysztof Choromanski;Mark Rowland;Vikas Sindhwani;Richard E. Turner

  • The Mirage of Action-Dependent Baselines in Reinforcement Learning.

    George Tucker;Surya Bhupatiraju;Shixiang Gu;Richard E. Turner

  • Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

    Aapo Hyvärinen;Hiroaki Sasaki;Richard E. Turner

Frequent Co-Authors

José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Maneesh Sahani
Maneesh Sahani University College London
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Shixiang Gu
Shixiang Gu Google (United States)
Brian C. J. Moore
Brian C. J. Moore University of Cambridge
Sergey Levine
Sergey Levine University of California, Berkeley
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Roy D. Patterson
Roy D. Patterson University of Cambridge
Adrian Weller
Adrian Weller University of Cambridge

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