Richard E Turner

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

Achievements

+8 more ↓

🏃 Academic Marathon (11) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (3) 🐣 Hot Topic Early Bird

🏃 Academic Marathon (11) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🗃️ Keyword Collector (88) 💎 Century Club (25) 🔥 Unstoppable (6) 📈 Trend Setter ⚡ Prolific Year (6)

Conferences

NIPS (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

Differential Privacy (1)

Keywords

variational inference (9) gaussian process (6) sample efficiency (3) expectation propagation (3) approximate inference (2) continual learning (2) hyperparameter learning (2) active learning (2) bayesian deep learning (2) neural process (2) bayesian inference (2) kullback-leibler divergence (2) time series (2) transfer learning (1) probabilistic modeling (1) reinforcement learning (1) deep reinforcement learning (1) online learning (1) few-shot learning (1) representation learning (1)

Papers

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