Turner, Richard

30 publications

NeurIPS 2023 Geometric Neural Diffusion Processes Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard Turner

NeurIPS 2023 Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures Runa Eschenhagen, Alexander Immer, Richard Turner, Frank Schneider, Philipp Hennig

NeurIPS 2023 PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard Turner, Johannes Brandstetter

NeurIPS 2022 Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard Turner

NeurIPS 2021 Collapsed Variational Bounds for Bayesian Neural Networks Marcin Tomczak, Siddharth Swaroop, Andrew Foong, Richard Turner

NeurIPS 2021 How Tight Can PAC-Bayes Be in the Small Data Regime? Andrew Foong, Wessel Bruinsma, David Burt, Richard Turner

NeurIPS 2021 Memory Efficient Meta-Learning with Large Images John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard Turner

ICLR 2020 Conservative Uncertainty Estimation by Fitting Prior Networks Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner

NeurIPS 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

ICMLW 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

NeurIPS 2020 Efficient Low Rank Gaussian Variational Inference for Neural Networks Marcin Tomczak, Siddharth Swaroop, Richard Turner

AISTATS 2020 Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Jan Stuehmer, Richard Turner, Sebastian Nowozin

NeurIPS 2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes Andrew Foong, Wessel Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard Turner

NeurIPS 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Foong, David Burt, Yingzhen Li, Richard Turner

ICML 2020 Scalable Exact Inference in Multi-Output Gaussian Processes Wessel Bruinsma, Eric Perim, William Tebbutt, Scott Hosking, Arno Solin, Richard Turner

ICML 2020 TaskNorm: Rethinking Batch Normalization for Meta-Learning John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard Turner

NeurIPS 2020 VAEM: A Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, Cheng Zhang

ICLR 2019 Meta-Learning Probabilistic Inference for Prediction Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard Turner

AISTATS 2019 Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning Aapo Hyvarinen, Hiroaki Sasaki, Richard Turner

JMLR 2018 Invariant Models for Causal Transfer Learning Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters

ICML 2018 Structured Evolution with Compact Architectures for Scalable Policy Optimization Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard Turner, Adrian Weller

ICML 2018 The Mirage of Action-Dependent Baselines in Reinforcement Learning George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard Turner, Zoubin Ghahramani, Sergey Levine

ICML 2017 Magnetic Hamiltonian Monte Carlo Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

ICML 2016 Black-Box Alpha Divergence Minimization Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner

ICML 2016 Deep Gaussian Processes for Regression Using Approximate Expectation Propagation Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner

ICML 2015 Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs Yarin Gal, Richard Turner

NeurIPS 2011 Probabilistic Amplitude and Frequency Demodulation Richard Turner, Maneesh Sahani

NeurIPS 2009 Occlusive Components Analysis Jörg Lücke, Richard Turner, Maneesh Sahani, Marc Henniges

NeurIPS 2007 Modeling Natural Sounds with Modulation Cascade Processes Richard Turner, Maneesh Sahani

NeurIPS 2007 On Sparsity and Overcompleteness in Image Models Pietro Berkes, Richard Turner, Maneesh Sahani