Richard E. Turner
30 papers · 2014–2025 · 8 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (41) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
🔬
Deep Specialist
(14)
🧭
Keyword Pioneer
🌈
Renaissance Researcher
(7)
🔥
Unstoppable · Bronze
❓
The Questioner
⚡
Prolific Year · Prolific
🚀
Conference Pioneer
💎
Century Club · Bronze
🗃️
Keyword Collector
(133)
Top co-authors
Research topics
Machine Learning
(28)
Optimization & Theory
(20)
Deep Learning
(17)
Core Methods
(13)
Models
(10)
Learning Types
(8)
Artificial Intelligence
(7)
Architectures
(6)
Application Areas
(5)
Bayesian & Probabilistic · Machine Learning
(5)
Bayesian & Probabilistic · Artificial Intelligence
(4)
Mathematics & Optimization
(3)
Optimization
(3)
Learning Paradigms · Machine Learning
(2)
Learning Paradigms · Artificial Intelligence
(2)
Keywords
gaussian process
(9)
variational inference
(8)
generative model
(4)
bayesian inference
(3)
continual learning
(3)
neural process
(3)
approximate inference
(3)
catastrophic forgetting
(2)
expectation propagation
(2)
translation equivariance
(2)
pseudo-point approximation
(2)
natural gradient
(2)
few-shot learning
(2)
diffusion model
(2)
bayesian learning
(1)
sample efficiency
(1)
maximum entropy
(1)
probabilistic modeling
(1)
representation learning
(1)
optimal transport
(1)
Papers
Bayesian Circular Regression with von Mises Quasi-Processes
AISTATS 2025
Influence Functions for Scalable Data Attribution in Diffusion Models
ICLR 2025
Linear Transformer Topological Masking with Graph Random Features
ICLR 2025
Variance-Reducing Couplings for Random Features
ICLR 2025
A Generative Model of Symmetry Transformations
NeurIPS 2024
Approximately Equivariant Neural Processes
NeurIPS 2024
Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects
MLHC 2024
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
ICML 2024
Fearless Stochasticity in Expectation Propagation
NeurIPS 2024
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
AISTATS 2024
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
NeurIPS 2024
Noise-Aware Differentially Private Regression via Meta-Learning
NeurIPS 2024
On conditional diffusion models for PDE simulations
NeurIPS 2024
Optimising Distributions with Natural Gradient Surrogates
AISTATS 2024
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants
ICML 2024
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC
ICML 2024
Translation Equivariant Transformer Neural Processes
ICML 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
Continual Learning with Adaptive Weights (CLAW)
ICLR 2020
Convolutional Conditional Neural Processes
ICLR 2020
Deterministic Variational Inference for Robust Bayesian Neural Networks
ICLR 2019
The Gaussian Process Autoregressive Regression Model (GPAR)
AISTATS 2019
Gaussian Process Behaviour in Wide Deep Neural Networks
ICLR 2018
Gradient Estimators for Implicit Models
ICLR 2018
Variational Continual Learning
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