← Authors

Richard E. Turner

30 papers · 2014–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) πŸƒ Academic Marathon (11) πŸ—ΊοΈ Taxonomy Completionist (12) 🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (6) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ƒοΈ Keyword Collector (69) ⚑ Prolific Year (13) πŸš€ Conference Pioneer πŸ’Ž Century Club (30) πŸ”₯ Unstoppable (9) ❓ The Questioner

Conferences

ICLR (9) NIPS (6) AISTATS (5) ICML (5) JMLR (2) ICCV (1) MLHC (1) UAI (1)

Top co-authors

Richard E Turner (6) JosΓ© Miguel HernΓ‘ndez-Lobato (4) James Requeima (4) Stratis Markou (3) Wessel P. Bruinsma (3) Matthew Ashman (3) Jonathan So (3) Adrian Weller (3) Cristiana Diaconu (3) Runa Eschenhagen (3)

Research topics

Differential Privacy (1)

Keywords

gaussian process (5) variational inference (5) generative model (3) approximate inference (2) neural process (2) natural gradient (2) expectation propagation (2) pseudo-point approximation (2) few-shot learning (1) bayesian inference (1) sample efficiency (1) sparse coding (1) gaussian processes (1) data augmentation (1) representation learning (1) probabilistic modeling (1) continual learning (1) group theory (1) signal processing (1) spatio-temporal modeling (1)

Papers

Linear Transformer Topological Masking with Graph Random Features ICLR 2025
Bayesian Circular Regression with von Mises Quasi-Processes AISTATS 2025
Influence Functions for Scalable Data Attribution in Diffusion Models ICLR 2025
Variance-Reducing Couplings for Random Features ICLR 2025
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language NIPS 2024
Fearless Stochasticity in Expectation Propagation NIPS 2024
On conditional diffusion models for PDE simulations NIPS 2024
A Generative Model of Symmetry Transformations NIPS 2024
Approximately Equivariant Neural Processes NIPS 2024
Optimising Distributions with Natural Gradient Surrogates AISTATS 2024
Identifiable Feature Learning for Spatial Data with Nonlinear ICA AISTATS 2024
Translation Equivariant Transformer Neural Processes ICML 2024
Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects MLHC 2024
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC ICML 2024
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective ICML 2024
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants ICML 2024
Noise-Aware Differentially Private Regression via Meta-Learning NIPS 2024
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning ICCV 2023
Modelling Non-Smooth Signals with Complex Spectral Structure AISTATS 2022
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes UAI 2021
Convolutional Conditional Neural Processes ICLR 2020
Continual Learning with Adaptive Weights (CLAW) ICLR 2020
The Gaussian Process Autoregressive Regression Model (GPAR) AISTATS 2019
Deterministic Variational Inference for Robust Bayesian Neural Networks ICLR 2019
Variational Continual Learning ICLR 2018
Gaussian Process Behaviour in Wide Deep Neural Networks ICLR 2018
Gradient Estimators for Implicit Models ICLR 2018
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation JMLR 2017
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control ICML 2017
Efficient Occlusive Components Analysis JMLR 2014