Richard E. Turner

Affiliations:
  • University of Cambridge, Department of Engineering, UK


According to our database1, Richard E. Turner authored at least 148 papers between 2007 and 2024.

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Bibliography

2024
On conditional diffusion models for PDE simulations.
CoRR, 2024

Influence Functions for Scalable Data Attribution in Diffusion Models.
CoRR, 2024

Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data.
CoRR, 2024

Linear Transformer Topological Masking with Graph Random Features.
CoRR, 2024

AI for operational methane emitter monitoring from space.
CoRR, 2024

Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models.
CoRR, 2024

In-Context In-Context Learning with Transformer Neural Processes.
CoRR, 2024

Approximately Equivariant Neural Processes.
CoRR, 2024

von Mises Quasi-Processes for Bayesian Circular Regression.
CoRR, 2024

Noise-Aware Differentially Private Regression via Meta-Learning.
CoRR, 2024

Fearless Stochasticity in Expectation Propagation.
CoRR, 2024

Variance-Reducing Couplings for Random Features: Perspectives from Optimal Transport.
CoRR, 2024

Aurora: A Foundation Model of the Atmosphere.
CoRR, 2024

LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language.
CoRR, 2024

Aardvark Weather: end-to-end data-driven weather forecasting.
CoRR, 2024

SportsNGEN: Sustained Generation of Multi-player Sports Gameplay.
CoRR, 2024

A Generative Model of Symmetry Transformations.
CoRR, 2024

Denoising Diffusion Probabilistic Models in Six Simple Steps.
CoRR, 2024

Transformer Neural Autoregressive Flows.
CoRR, 2024

SportsNGEN: Sustained Generation of Realistic Multi-Player Sports Gameplay.
Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support, 2024

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Translation Equivariant Transformer Neural Processes.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimising Distributions with Natural Gradient Surrogates.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Identifiable Feature Learning for Spatial Data with Nonlinear ICA.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
On the Efficacy of Differentially Private Few-shot Image Classification.
Trans. Mach. Learn. Res., 2023

Differentially private partitioned variational inference.
Trans. Mach. Learn. Res., 2023

Improving Continual Learning by Accurate Gradient Reconstructions of the Past.
Trans. Mach. Learn. Res., 2023

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets.
CoRR, 2023

Diffusion-Augmented Neural Processes.
CoRR, 2023

Sim2Real for Environmental Neural Processes.
CoRR, 2023

Beyond Intuition, a Framework for Applying GPs to Real-World Data.
CoRR, 2023

An Introduction to Transformers.
CoRR, 2023

Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants.
Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

Geometric Neural Diffusion Processes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Autoregressive Conditional Neural Processes.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation.
Proceedings of the Conference on Lifelong Learning Agents, 2023

2022
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement.
CoRR, 2022

Ice Core Dating using Probabilistic Programming.
CoRR, 2022

Kernel Learning for Explainable Climate Science.
CoRR, 2022

The Neural Process Family: Survey, Applications and Perspectives.
CoRR, 2022

Challenges and Pitfalls of Bayesian Unlearning.
CoRR, 2022

Practical Conditional Neural Processes Via Tractable Dependent Predictions.
CoRR, 2022

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning.
CoRR, 2022

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Practical Conditional Neural Process Via Tractable Dependent Predictions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Bayesian Neural Network Priors Revisited.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners.
Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

Multi-disciplinary fairness considerations in machine learning for clinical trials.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Continual Novelty Detection.
Proceedings of the Conference on Lifelong Learning Agents, 2022

Modelling Non-Smooth Signals with Complex Spectral Structure.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Efficient Gaussian Neural Processes for Regression.
CoRR, 2021

Contextual HyperNetworks for Novel Feature Adaptation.
CoRR, 2021

Bayesian Neural Network Priors Revisited.
CoRR, 2021

Convolutional conditional neural processes for local climate downscaling.
CoRR, 2021

The Gaussian Neural Process.
CoRR, 2021

Combining pseudo-point and state space approximations for sum-separable Gaussian Processes.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Collapsed Variational Bounds for Bayesian Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

How Tight Can PAC-Bayes be in the Small Data Regime?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Memory Efficient Meta-Learning with Large Images.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalized Variational Continual Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Sparse Gaussian Process Variational Autoencoders.
CoRR, 2020

Interpreting Spatially Infinite Generative Models.
CoRR, 2020

Diagnostic Questions: The NeurIPS 2020 Education Challenge.
CoRR, 2020

Efficient Low Rank Gaussian Variational Inference for Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Continual Deep Learning by Functional Regularisation of Memorable Past.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Expressiveness of Approximate Inference in Bayesian Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge.
Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

Scalable Exact Inference in Multi-Output Gaussian Processes.
Proceedings of the 37th International Conference on Machine Learning, 2020

TaskNorm: Rethinking Batch Normalization for Meta-Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Conservative Uncertainty Estimation By Fitting Prior Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

Continual Learning with Adaptive Weights (CLAW).
Proceedings of the 8th International Conference on Learning Representations, 2020

Convolutional Conditional Neural Processes.
Proceedings of the 8th International Conference on Learning Representations, 2020

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.
PLoS Comput. Biol., 2019

Differentially Private Federated Variational Inference.
CoRR, 2019

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks.
CoRR, 2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model.
CoRR, 2019

'In-Between' Uncertainty in Bayesian Neural Networks.
CoRR, 2019

Fast computation of loudness using a deep neural network.
CoRR, 2019

Improving and Understanding Variational Continual Learning.
CoRR, 2019

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Practical Deep Learning with Bayesian Principles.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deterministic Variational Inference for Robust Bayesian Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

Meta-Learning Probabilistic Inference for Prediction.
Proceedings of the 7th International Conference on Learning Representations, 2019

Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

The Gaussian Process Autoregressive Regression Model (GPAR).
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018
Invariant Models for Causal Transfer Learning.
J. Mach. Learn. Res., 2018

Partitioned Variational Inference: A unified framework encompassing federated and continual learning.
CoRR, 2018

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks.
CoRR, 2018

Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning.
CoRR, 2018

Infinite-Horizon Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Geometrically Coupled Monte Carlo Sampling.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Structured Evolution with Compact Architectures for Scalable Policy Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Continual Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Gaussian Process Behaviour in Wide Deep Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Gradient Estimators for Implicit Models.
Proceedings of the 6th International Conference on Learning Representations, 2018

The Geometry of Random Features.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation.
J. Mach. Learn. Res., 2017

Approximate Inference with Amortised MCMC.
CoRR, 2017

Discriminative k-shot learning using probabilistic models.
CoRR, 2017

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Streaming Sparse Gaussian Process Approximations.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Magnetic Hamiltonian Monte Carlo.
Proceedings of the 34th International Conference on Machine Learning, 2017

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control.
Proceedings of the 34th International Conference on Machine Learning, 2017

Tuning Recurrent Neural Networks with Reinforcement Learning.
Proceedings of the 5th International Conference on Learning Representations, 2017

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic.
Proceedings of the 5th International Conference on Learning Representations, 2017

The Multivariate Generalised von Mises Distribution: Inference and Applications.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Variational Inference with Rényi Divergence.
CoRR, 2016

A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation.
CoRR, 2016

Rényi Divergence Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Modelling time series via automatic learning of basis functions.
Proceedings of the 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016

Black-Box Alpha Divergence Minimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
Proceedings of the 33nd International Conference on Machine Learning, 2016

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
A multi-label approach to target prediction taking ligand promiscuity into account.
J. Cheminformatics, 2015

Denoising without access to clean data using a partitioned autoencoder.
CoRR, 2015

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Stochastic Expectation Propagation.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Neural Adaptive Sequential Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Modelling of complex signals using gaussian processes.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Time-Frequency Analysis as Probabilistic Inference.
IEEE Trans. Signal Process., 2014

Efficient occlusive components analysis.
J. Mach. Learn. Res., 2014

Target Fishing: A Single-Label or Multi-Label Problem?
CoRR, 2014

Tree-structured Gaussian Process Approximations.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2012
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference.
Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2011
Demodulation as Probabilistic Inference.
IEEE ACM Trans. Audio Speech Lang. Process., 2011

Probabilistic amplitude and frequency demodulation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Spoken Nursery Rhymes Have a Fractal Rhythmic Structure - Evidence from Patterns of Slow Amplitude Modulation (AM).
Proceedings of the 33th Annual Meeting of the Cognitive Science Society, 2011

2010
Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation.
Proceedings of the IEEE International Conference on Acoustics, 2010

2009
A Structured Model of Video Reproduces Primary Visual Cortical Organisation.
PLoS Comput. Biol., 2009

Occlusive Components Analysis.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007
A Maximum-Likelihood Interpretation for Slow Feature Analysis.
Neural Comput., 2007

Modeling Natural Sounds with Modulation Cascade Processes.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

On Sparsity and Overcompleteness in Image Models.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Probabilistic Amplitude Demodulation.
Proceedings of the Independent Component Analysis and Signal Separation, 2007


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