Turner, Richard E.

81 publications

AISTATS 2025 Bayesian Circular Regression with Von Mises Quasi-Processes Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

NeurIPS 2025 Distributional Training Data Attribution: What Do Influence Functions Sample? Bruno Kacper Mlodozeniec, Isaac Reid, Samuel Power, David Krueger, Murat A Erdogdu, Richard E. Turner, Roger Baker Grosse

TMLR 2025 Efficient Few-Shot Continual Learning in Vision-Language Models Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner

NeurIPS 2025 Estimating Interventional Distributions with Uncertain Causal Graphs Through Meta-Learning Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk

ICML 2025 Gridded Transformer Neural Processes for Spatio-Temporal Data Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E Turner

ICLR 2025 Influence Functions for Scalable Data Attribution in Diffusion Models Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner

ICLR 2025 Linear Transformer Topological Masking with Graph Random Features Isaac Reid, Kumar Avinava Dubey, Deepali Jain, William F Whitney, Amr Ahmed, Joshua Ainslie, Alex Bewley, Mithun George Jacob, Aranyak Mehta, David Rendleman, Connor Schenck, Richard E. Turner, René Wagner, Adrian Weller, Krzysztof Marcin Choromanski

ICML 2025 Position: Probabilistic Modelling Is Sufficient for Causal Inference Bruno Kacper Mlodozeniec, David Krueger, Richard E Turner

NeurIPS 2025 Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing Its Preconditioner Runa Eschenhagen, Aaron Defazio, Tsung-Hsien Lee, Richard E. Turner, Hao-Jun Michael Shi

TMLR 2025 Tighter Sparse Variational Gaussian Processes Thang D Bui, Matthew Ashman, Richard E. Turner

ICLR 2025 Variance-Reducing Couplings for Random Features Isaac Reid, Stratis Markou, Krzysztof Marcin Choromanski, Richard E. Turner, Adrian Weller

NeurIPS 2024 A Generative Model of Symmetry Transformations James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric Nalisnick, José Miguel Hernández-Lobato

NeurIPS 2024 Approximately Equivariant Neural Processes Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel Bruinsma, Richard E. Turner

MLHC 2024 Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects Isabel Chien, Cliff Wong, Zelalem Gero, Jaspreet Bagga, Risa Ueno, Richard E. Turner, Roshanthi K. Weerasinghe, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon, Javier González Hernández

ICML 2024 Can We Remove the Square-Root in Adaptive Gradient Methods? a Second-Order Perspective Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani

NeurIPS 2024 Fearless Stochasticity in Expectation Propagation Jonathan So, Richard E. Turner

ICLRW 2024 Guided Autoregressive Diffusion Models with Applications to PDE Simulation Federico Bergamin, Cristiana Diaconu, Aliaksandra Shysheya, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu

AISTATS 2024 Identifiable Feature Learning for Spatial Data with Nonlinear ICA Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen

NeurIPS 2024 LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud

ICMLW 2024 LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language James Requeima, John F Bronskill, Dami Choi, Richard E. Turner, David Duvenaud

NeurIPS 2024 Noise-Aware Differentially Private Regression via Meta-Learning Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner

NeurIPS 2024 On Conditional Diffusion Models for PDE Simulations Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu

AISTATS 2024 Optimising Distributions with Natural Gradient Surrogates Jonathan So, Richard E. Turner

ICML 2024 Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants Isabel Chien, Wessel P Bruinsma, Javier Gonzalez, Richard E. Turner

ICML 2024 Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

ICMLW 2024 Transformer Neural Autoregressive Flows Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann, Richard E. Turner

ICML 2024 Translation Equivariant Transformer Neural Processes Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P Bruinsma, Richard E. Turner

ICMLW 2024 Von Mises Quasi-Processes for Bayesian Circular Regression Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

ICLR 2023 Autoregressive Conditional Neural Processes Wessel Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom Andersson, Anna Vaughan, Anthony Buonomo, Scott Hosking, Richard E Turner

ICMLW 2023 Beyond Intuition, a Framework for Applying GPs to Real-World Data Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, S. T. John, Hong Ge, Richard E Turner

CoLLAs 2023 Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner

ICLRW 2023 Differentially Private Federated Few-Shot Image Classification Aliaksandra Shysheya, Marlon Tobaben, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

TMLR 2023 Differentially Private Partitioned Variational Inference Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E Turner, Antti Honkela

ICLR 2023 FiT: Parameter Efficient Few-Shot Transfer Learning for Personalized and Federated Image Classification Aliaksandra Shysheya, John F Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner

ICCV 2023 First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner

TMLR 2023 Improving Continual Learning by Accurate Gradient Reconstructions of the past Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

ICMLW 2023 Modeling Accurate Long Rollouts with Temporal Neural PDE Solvers Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E Turner, Johannes Brandstetter

TMLR 2023 On the Efficacy of Differentially Private Few-Shot Image Classification Marlon Tobaben, Aliaksandra Shysheya, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

ICLRW 2023 On the Efficacy of Differentially Private Few-Shot Image Classification Marlon Tobaben, Aliaksandra Shysheya, John F Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Beguelin, Richard E Turner, Antti Honkela

ICLRW 2023 Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants Isabel Chien, Javier Gonzalez Hernandez, Richard E Turner

NeurIPSW 2023 Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

AISTATS 2022 Modelling Non-Smooth Signals with Complex Spectral Structure Wessel P. Bruinsma, Martin Tegnér, Richard E. Turner

NeurIPSW 2022 Adversarial Attacks Are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners Elre Talea Oldewage, John F Bronskill, Richard E Turner

ICLR 2022 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

NeurIPSW 2022 Contextual Squeeze-and-Excitation Massimiliano Patacchiola, John F Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E Turner

CoLLAs 2022 Continual Novelty Detection Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner

NeurIPSW 2022 FiT: Parameter Efficient Few-Shot Transfer Learning Aliaksandra Shysheya, John F Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner

ICLR 2022 Practical Conditional Neural Process via Tractable Dependent Predictions Stratis Markou, James Requeima, Wessel Bruinsma, Anna Vaughan, Richard E Turner

ICMLW 2021 Attacking Few-Shot Classifiers with Adversarial Support Poisoning Elre Talea Oldewage, John F Bronskill, Richard E Turner

UAI 2021 Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes Will Tebbutt, Arno Solin, Richard E. Turner

ICLR 2021 Generalized Variational Continual Learning Noel Loo, Siddharth Swaroop, Richard E Turner

NeurIPSW 2020 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

ICMLW 2020 Combining Variational Continual Learning with FiLM Layers Noel Loo, Siddharth Swaroop, Richard E Turner

ICLR 2020 Continual Learning with Adaptive Weights (CLAW) Tameem Adel, Han Zhao, Richard E. Turner

ICLR 2020 Convolutional Conditional Neural Processes Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner

ICMLW 2020 VAEM: A Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

ICLR 2019 Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

NeurIPS 2019 Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E Turner

NeurIPS 2019 Icebreaker: Element-Wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E Turner, José Miguel Hernández-Lobato, Cheng Zhang

NeurIPS 2019 Practical Deep Learning with Bayesian Principles Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E Turner, Rio Yokota

AISTATS 2019 The Gaussian Process Autoregressive Regression Model (GPAR) James Requeima, William Tebbutt, Wessel Bruinsma, Richard E. Turner

ICLR 2018 Gaussian Process Behaviour in Wide Deep Neural Networks Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani

NeurIPS 2018 Geometrically Coupled Monte Carlo Sampling Mark Rowland, Krzysztof M Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E Turner, Adrian Weller

ICLR 2018 Gradient Estimators for Implicit Models Yingzhen Li, Richard E. Turner

NeurIPS 2018 Infinite-Horizon Gaussian Processes Arno Solin, James Hensman, Richard E Turner

AISTATS 2018 The Geometry of Random Features Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller

ICLR 2018 Variational Continual Learning Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner

JMLR 2017 A Unifying Framework for Gaussian Process Pseudo-Point Approximations Using Power Expectation Propagation Thang D. Bui, Josiah Yan, Richard E. Turner

NeurIPS 2017 Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning Shixiang Gu, Timothy Lillicrap, Richard E Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine

ICLR 2017 Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critic Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine

ICML 2017 Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-Control Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck

NeurIPS 2017 Streaming Sparse Gaussian Process Approximations Thang D Bui, Cuong Nguyen, Richard E Turner

AAAI 2017 The Multivariate Generalised Von Mises Distribution: Inference and Applications Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner

ICLR 2017 Tuning Recurrent Neural Networks with Reinforcement Learning Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck

AISTATS 2016 On Sparse Variational Methods and the Kullback-Leibler Divergence Between Stochastic Processes Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

NeurIPS 2016 Rényi Divergence Variational Inference Yingzhen Li, Richard E Turner

NeurIPS 2015 Learning Stationary Time Series Using Gaussian Processes with Nonparametric Kernels Felipe Tobar, Thang D Bui, Richard E Turner

NeurIPS 2015 Neural Adaptive Sequential Monte Carlo Shixiang Gu, Zoubin Ghahramani, Richard E Turner

NeurIPS 2015 Stochastic Expectation Propagation Yingzhen Li, José Miguel Hernández-Lobato, Richard E Turner

JMLR 2014 Efficient Occlusive Components Analysis Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke

NeurIPS 2014 Tree-Structured Gaussian Process Approximations Thang D Bui, Richard E Turner