Richard E. Turner
30 papers · 2014–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (12) π Conference Polyglot (8)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
ποΈ
Keyword Collector
(69)
β‘
Prolific Year
(13)
π
Conference Pioneer
π
Century Club
(30)
π₯
Unstoppable
(9)
β
The Questioner
Conferences
ICLR (9)
NIPS (6)
AISTATS (5)
ICML (5)
JMLR (2)
ICCV (1)
MLHC (1)
UAI (1)
Top co-authors
Research topics
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