Richard E. Turner
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
51
Venues
9
Active years
2010–2025
Best venue rank
A*
Where they publish
Papers
51 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | Bayesian Circular Regression with von Mises Quasi-Processes. | Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman |
| 2025 | ICLR | Influence Functions for Scalable Data Attribution in Diffusion Models. | Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner |
| 2025 | ICLR | 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, Ren Wagner, Adrian Weller, Krzysztof Marcin Choromanski |
| 2025 | ICLR | Variance-Reducing Couplings for Random Features. | Isaac Reid, Stratis Markou, Krzysztof Marcin Choromanski, Richard E. Turner, Adrian Weller |
| 2025 | ICML | Gridded Transformer Neural Processes for Spatio-Temporal Data. | Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner |
| 2025 | ICML | Position: Probabilistic Modelling is Sufficient for Causal Inference. | Bruno Kacper Mlodozeniec, David Krueger, Richard E. Turner |
| 2024 | AISTATS | Identifiable Feature Learning for Spatial Data with Nonlinear ICA. | Hermanni Hlv, Jonathan So, Richard E. Turner, Aapo Hyvrinen |
| 2024 | AISTATS | Optimising Distributions with Natural Gradient Surrogates. | Jonathan So, Richard E. Turner |
| 2024 | ICML | Translation Equivariant Transformer Neural Processes. | Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner |
| 2024 | ICML | Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. | Isabel Chien, Wessel P. Bruinsma, Javier Gonzlez Hernndez, Richard E. Turner |
| 2024 | ICML | 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 |
| 2024 | ICML | 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 |
| 2023 | ICCV | First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. | Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner |
| 2023 | ICLR | Autoregressive Conditional Neural Processes. | Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner |
| 2023 | ICLR | FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. | Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner |
| 2022 | AISTATS | Modelling Non-Smooth Signals with Complex Spectral Structure. | Wessel P. Bruinsma, Martin Tegner, Richard E. Turner |
| 2022 | ICLR | Bayesian Neural Network Priors Revisited. | Vincent Fortuin, Adri Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rtsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison |
| 2022 | ICLR | Practical Conditional Neural Process Via Tractable Dependent Predictions. | Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner |
| 2021 | ICLR | Generalized Variational Continual Learning. | Noel Loo, Siddharth Swaroop, Richard E. Turner |
| 2021 | UAI | Combining pseudo-point and state space approximations for sum-separable Gaussian Processes. | Will Tebbutt, Arno Solin, Richard E. Turner |
| 2020 | AISTATS | Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. | Jan Stuehmer, Richard E. Turner, Sebastian Nowozin |
| 2020 | ICLR | Convolutional Conditional Neural Processes. | Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner |
| 2020 | ICLR | Continual Learning with Adaptive Weights (CLAW). | Tameem Adel, Han Zhao, Richard E. Turner |
| 2020 | ICLR | Conservative Uncertainty Estimation By Fitting Prior Networks. | Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner |
| 2020 | ICML | TaskNorm: Rethinking Batch Normalization for Meta-Learning. | John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner |
| 2020 | ICML | Scalable Exact Inference in Multi-Output Gaussian Processes. | Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner |
| 2019 | AISTATS | Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. | Aapo Hyvrinen, Hiroaki Sasaki, Richard E. Turner |
| 2019 | AISTATS | The Gaussian Process Autoregressive Regression Model (GPAR). | James Requeima, William Tebbutt, Wessel P. Bruinsma, Richard E. Turner |
| 2019 | EMNLP | Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling. | Bo-Hsiang Tseng, Marek Rei, Pawel Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen |
| 2019 | ICLR | Meta-Learning Probabilistic Inference for Prediction. | Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner |
| 2019 | ICLR | Deterministic Variational Inference for Robust Bayesian Neural Networks. | Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, Jos Miguel Hernndez-Lobato, Alexander L. Gaunt |
| 2018 | AISTATS | The Geometry of Random Features. | Krzysztof Choromanski, Mark Rowland, Tams Sarls, Vikas Sindhwani, Richard E. Turner, Adrian Weller |
| 2018 | ICLR | Gradient Estimators for Implicit Models. | Yingzhen Li, Richard E. Turner |
| 2018 | ICLR | Gaussian Process Behaviour in Wide Deep Neural Networks. | Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani |
| 2018 | ICLR | Variational Continual Learning. | Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner |
| 2018 | ICLR | The Mirage of Action-Dependent Baselines in Reinforcement Learning. | George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine |
| 2018 | ICML | Structured Evolution with Compact Architectures for Scalable Policy Optimization. | Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller |
| 2018 | ICML | The Mirage of Action-Dependent Baselines in Reinforcement Learning. | George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine |
| 2017 | AAAI | The Multivariate Generalised von Mises Distribution: Inference and Applications. | Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner |
| 2017 | ICLR | Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. | Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine |
| 2017 | ICLR | Tuning Recurrent Neural Networks with Reinforcement Learning. | Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck |
| 2017 | ICML | Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. | Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, Jos Miguel Hernndez-Lobato, Richard E. Turner, Douglas Eck |
| 2017 | ICML | Magnetic Hamiltonian Monte Carlo. | Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner |
| 2016 | AISTATS | On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. | Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani |
| 2016 | ICML | Deep Gaussian Processes for Regression using Approximate Expectation Propagation. | Thang D. Bui, Daniel Hernndez-Lobato, Jos Miguel Hernndez-Lobato, Yingzhen Li, Richard E. Turner |
| 2016 | ICML | Black-Box Alpha Divergence Minimization. | Jos Miguel Hernndez-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernndez-Lobato, Richard E. Turner |
| 2015 | ICASSP | Modelling of complex signals using gaussian processes. | Felipe A. Tobar, Richard E. Turner |
| 2015 | ICML | Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. | Yarin Gal, Richard E. Turner |
| 2012 | ICASSP | Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. | Richard E. Turner, Maneesh Sahani |
| 2011 | CogSci | Spoken Nursery Rhymes Have a Fractal Rhythmic Structure - Evidence from Patterns of Slow Amplitude Modulation (AM). | Victoria Leong, Richard E. Turner, Michael Stone, Usha Goswami |
| 2010 | ICASSP | Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. | Richard E. Turner, Maneesh Sahani |