Richard E Turner
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Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
arXiv preprint arXiv:1710.10628, 2017
Q-prop: Sample-efficient policy gradient with an off-policy critic
S Gu, T Lillicrap, Z Ghahramani, RE Turner, S Levine
arXiv preprint arXiv:1611.02247, 2016
Two problems with variational expectation maximisation for time-series models
RE Turner, M Sahani
The processing and perception of size information in speech sounds
DRR Smith, RD Patterson, R Turner, H Kawahara, T Irino
The Journal of the Acoustical Society of America 117 (1), 305-318, 2005
Gaussian process behaviour in wide deep neural networks
Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani
International Conference on Learning Representations 4, 2018
R\'enyi Divergence Variational Inference
Y Li, RE Turner
arXiv preprint arXiv:1602.02311, 2016
Deep Gaussian processes for regression using approximate expectation propagation
T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner
International conference on machine learning, 1472-1481, 2016
Black-box alpha divergence minimization
J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ...
International Conference on Machine Learning, 1511-1520, 2016
Interpolated policy gradient: Merging on-policy and off-policy gradient estimation for deep reinforcement learning
S Gu, T Lillicrap, Z Ghahramani, RE Turner, B Schölkopf, S Levine
arXiv preprint arXiv:1706.00387, 2017
Invariant models for causal transfer learning
M Rojas-Carulla, B Schölkopf, R Turner, J Peters
The Journal of Machine Learning Research 19 (1), 1309-1342, 2018
Meta-learning probabilistic inference for prediction
J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner
arXiv preprint arXiv:1805.09921, 2018
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
RETZG Alexander G. Matthews, James Hensman
Proceedings of the 19th International Conference on Artificial Intelligence …, 2016
Neural adaptive sequential monte carlo
S Gu, Z Ghahramani, RE Turner
arXiv preprint arXiv:1506.03338, 2015
A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation
TD Bui, J Yan, RE Turner
The Journal of Machine Learning Research 18 (1), 3649-3720, 2017
Stochastic expectation propagation
Y Li, JM Hernández-Lobato, RE Turner
arXiv preprint arXiv:1506.04132, 2015
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck
International Conference on Machine Learning, 1645-1654, 2017
Practical deep learning with Bayesian principles
K Osawa, S Swaroop, A Jain, R Eschenhagen, RE Turner, R Yokota, ...
arXiv preprint arXiv:1906.02506, 2019
Deterministic variational inference for robust bayesian neural networks
A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt
arXiv preprint arXiv:1810.03958, 2018
Structured evolution with compact architectures for scalable policy optimization
K Choromanski, M Rowland, V Sindhwani, R Turner, A Weller
International Conference on Machine Learning, 970-978, 2018
Nonlinear ICA using auxiliary variables and generalized contrastive learning
A Hyvarinen, H Sasaki, R Turner
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
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Articles 1–20