Richard E. Turner

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 168

Top Venues

CoRR

NeurIPS

ICLR

AISTATS

Publications

Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR (2024)
Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner
Approximately Equivariant Neural Processes. CoRR (2024)
Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
Noise-Aware Differentially Private Regression via Meta-Learning. CoRR (2024)
Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. AISTATS (2024)
Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner
Aardvark Weather: end-to-end data-driven weather forecasting. CoRR (2024)
Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann, Richard E. Turner
Transformer Neural Autoregressive Flows. CoRR (2024)
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner
Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models. CoRR (2024)
Lachlan Thorpe, Lewis Bawden, Karanjot Vendal, John Bronskill, Richard E. Turner
SportsNGEN: Sustained Generation of Multi-player Sports Gameplay. CoRR (2024)
Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman
von Mises Quasi-Processes for Bayesian Circular Regression. CoRR (2024)
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
Translation Equivariant Transformer Neural Processes. CoRR (2024)
Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner
In-Context In-Context Learning with Transformer Neural Processes. CoRR (2024)
Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
Aurora: A Foundation Model of the Atmosphere. CoRR (2024)
Jonathan So, Richard E. Turner
Fearless Stochasticity in Expectation Propagation. CoRR (2024)
Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec
Denoising Diffusion Probabilistic Models in Six Simple Steps. CoRR (2024)
James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR (2024)
James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
A Generative Model of Symmetry Transformations. CoRR (2024)
Isaac Reid, Stratis Markou, Krzysztof Choromanski, Richard E. Turner, Adrian Weller
Variance-Reducing Couplings for Random Features: Perspectives from Optimal Transport. CoRR (2024)
Jonathan So, Richard E. Turner
Optimising Distributions with Natural Gradient Surrogates. AISTATS (2024)
Jonas Scholz, Tom R. Andersson, Anna Vaughan, James Requeima, Richard E. Turner
Sim2Real for Environmental Neural Processes. CoRR (2023)
Lorenzo Bonito, James Requeima, Aliaksandra Shysheya, Richard E. Turner
Diffusion-Augmented Neural Processes. CoRR (2023)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR (2023)
Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. CoRR (2023)
Richard E. Turner
An Introduction to Transformers. CoRR (2023)
Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR (2023)
Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, S. T. John, Hong Ge, Richard E. Turner
Beyond Intuition, a Framework for Applying GPs to Real-World Data. CoRR (2023)
Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR (2023)
Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023)
Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
Geometric Neural Diffusion Processes. CoRR (2023)
Jonathan So, Richard E. Turner
Optimising Distributions with Natural Gradient Surrogates. CoRR (2023)
Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
Geometric Neural Diffusion Processes. NeurIPS (2023)
Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. ICLR (2023)
Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
Autoregressive Conditional Neural Processes. ICLR (2023)
Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela
On the Efficacy of Differentially Private Few-shot Image Classification. Trans. Mach. Learn. Res. 2023 (2023)
Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. CoRR (2023)
Isabel Chien, Javier González Hernández, Richard E. Turner
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. TML4H (2023)
Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela
On the Efficacy of Differentially Private Few-shot Image Classification. CoRR (2023)
Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS (2023)
Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. CoLLAs (2023)
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. ICCV (2023)
Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
Autoregressive Conditional Neural Processes. CoRR (2023)
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023)
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. CoRR (2023)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS (2023)
Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner
Continual Novelty Detection. CoLLAs (2022)
Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
Practical Conditional Neural Process Via Tractable Dependent Predictions. ICLR (2022)
Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking
Kernel Learning for Explainable Climate Science. CoRR (2022)
Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yao
The Neural Process Family: Survey, Applications and Perspectives. CoRR (2022)
Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
Practical Conditional Neural Processes Via Tractable Dependent Predictions. CoRR (2022)
Wessel P. Bruinsma, Martin Tegner, Richard E. Turner
Modelling Non-Smooth Signals with Complex Spectral Structure. AISTATS (2022)
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. ICLR (2022)
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
Differentially private partitioned variational inference. CoRR (2022)
Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner
Challenges and Pitfalls of Bayesian Unlearning. CoRR (2022)
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
Ice Core Dating using Probabilistic Programming. CoRR (2022)
Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. CoRR (2022)
Elre T. Oldewage, John Bronskill, Richard E. Turner
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. ICBINB (2022)
Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew Lazzara, Daniel C. Jones, J. Scott Hosking, Richard E. Turner
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement. CoRR (2022)
Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus
Multi-disciplinary fairness considerations in machine learning for clinical trials. CoRR (2022)
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. CoRR (2022)
Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. NeurIPS (2022)
Elre T. Oldewage, John Bronskill, Richard E. Turner
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. CoRR (2022)
Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. CoRR (2022)
Wessel P. Bruinsma, Martin Tegner, Richard E. Turner
Modelling Non-Smooth Signals with Complex Spectral Structure. CoRR (2022)
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR (2021)
Noel Loo, Siddharth Swaroop, Richard E. Turner
Generalized Variational Continual Learning. ICLR (2021)
John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
Memory Efficient Meta-Learning with Large Images. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. CoRR (2021)
Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
Contextual HyperNetworks for Novel Feature Adaptation. CoRR (2021)
Marcin Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E. Turner
Collapsed Variational Bounds for Bayesian Neural Networks. NeurIPS (2021)
Will Tebbutt, Arno Solin, Richard E. Turner
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes. UAI (2021)
Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner
The Gaussian Neural Process. CoRR (2021)
Stratis Markou, James Requeima, Wessel Bruinsma, Richard E. Turner
Efficient Gaussian Neural Processes for Regression. CoRR (2021)
Anna Vaughan, William Tebbutt, J. Scott Hosking, Richard E. Turner
Convolutional conditional neural processes for local climate downscaling. CoRR (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
How Tight Can PAC-Bayes be in the Small Data Regime? CoRR (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS (2021)
Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner
Continual Novelty Detection. CoRR (2021)
Will Tebbutt, Arno Solin, Richard E. Turner
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes. CoRR (2021)
Wessel Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner
Scalable Exact Inference in Multi-Output Gaussian Processes. ICML (2020)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS (2020)
Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS (2020)
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR (2020)
Tameem Adel, Han Zhao, Richard E. Turner
Continual Learning with Adaptive Weights (CLAW). ICLR (2020)
John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner
TaskNorm: Rethinking Batch Normalization for Meta-Learning. CoRR (2020)
Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. CoRR (2020)
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) (2020)
Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman
Interpreting Spatially Infinite Generative Models. CoRR (2020)
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
Convolutional Conditional Neural Processes. ICLR (2020)
Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR (2020)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR (2020)
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS (2020)
John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner
TaskNorm: Rethinking Batch Normalization for Meta-Learning. ICML (2020)
Andrew Y. K. Foong, Wessel Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. NeurIPS (2020)
Marcin Tomczak, Siddharth Swaroop, Richard E. Turner
Efficient Low Rank Gaussian Variational Inference for Neural Networks. NeurIPS (2020)
Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner
Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR (2020)
Jan Stuehmer, Richard E. Turner, Sebastian Nowozin
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. AISTATS (2020)
Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
Sparse Gaussian Process Variational Autoencoders. CoRR (2020)
Noel Loo, Siddharth Swaroop, Richard E. Turner
Generalized Variational Continual Learning. CoRR (2020)
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner
Meta-Learning Probabilistic Inference for Prediction. ICLR (Poster) (2019)
Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore
Fast computation of loudness using a deep neural network. CoRR (2019)
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
Practical Deep Learning with Bayesian Principles. CoRR (2019)
Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
Practical Deep Learning with Bayesian Principles. NeurIPS (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Richard E. Turner