Richard E Turner

25 papers · 2014–2025 · 3 venues · across top CS/AI venues

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Achievements

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🗺️ Taxonomy Completionist (49) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird

🔬 Deep Specialist (10) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 📈 Trend Setter ⚡ Prolific Year · Productive 🔥 Unstoppable · Bronze 🗃️ Keyword Collector (124) 💎 Century Club · Bronze

Venues

NeurIPS (18) ICLR (5) ICML (2)

Top co-authors

Richard E. Turner (6) José Miguel Hernández-Lobato (4) James Requeima (4) Stratis Markou (3) Thang D Bui (3) Sebastian Nowozin (3) Adrian Weller (3) Wessel Bruinsma (3) Matthew Ashman (3) Cristiana Diaconu (3)

Research topics

Machine Learning (22) Artificial Intelligence (12) Deep Learning (12) Optimization & Theory (12) Learning Types · Machine Learning (11) Bayesian & Probabilistic · Artificial Intelligence (8) Bayesian & Probabilistic · Machine Learning (8) Core Methods (8) Models (8) Learning Paradigms · Artificial Intelligence (5) Learning Paradigms · Machine Learning (4) Architectures (3) Mathematics & Optimization (2) Application Areas (2) Learning Types · Deep Learning (2)

Keywords

variational inference (10) gaussian process (6) expectation propagation (3) sample efficiency (3) conditional neural process (3) bayesian deep learning (2) variational autoencoder (2) neural process (2) time series (2) bayesian inference (2) continual learning (2) state space model (2) few-shot learning (2) bayesian neural network (2) hyperparameter learning (2) probabilistic modeling (2) approximate inference (2) transfer learning (2) active learning (2) kullback-leibler divergence (2)

Papers

Gridded Transformer Neural Processes for Spatio-Temporal Data ICML 2025 Position: Probabilistic Modelling is Sufficient for Causal Inference ICML 2025 A Generative Model of Symmetry Transformations NeurIPS 2024 Approximately Equivariant Neural Processes NeurIPS 2024 Fearless Stochasticity in Expectation Propagation NeurIPS 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 Autoregressive Conditional Neural Processes ICLR 2023 FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification ICLR 2023 Bayesian Neural Network Priors Revisited ICLR 2022 Practical Conditional Neural Process Via Tractable Dependent Predictions ICLR 2022 Generalized Variational Continual Learning ICLR 2021 Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes NeurIPS 2019 Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model NeurIPS 2019 Practical Deep Learning with Bayesian Principles NeurIPS 2019 Geometrically Coupled Monte Carlo Sampling NeurIPS 2018 Infinite-Horizon Gaussian Processes NeurIPS 2018 Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning NeurIPS 2017 Streaming Sparse Gaussian Process Approximations NeurIPS 2017 Rényi Divergence Variational Inference NeurIPS 2016 Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels NeurIPS 2015 Neural Adaptive Sequential Monte Carlo NeurIPS 2015 Stochastic Expectation Propagation NeurIPS 2015 Tree-structured Gaussian Process Approximations NeurIPS 2014