Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDRichard E. Turner
Person information
- affiliation: University of Cambridge, Department of Engineering, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2022
[c65]Wessel P. Bruinsma, Martin Tegner, Richard E. Turner:
Modelling Non-Smooth Signals with Complex Spectral Structure. AISTATS 2022: 5166-5195- [c64]Isabel Chien, Nina Deliu
, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus:
Multi-disciplinary fairness considerations in machine learning for clinical trials. FAccT 2022: 906-924 - [c63]Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. ICLR 2022 - [c62]Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner:
Practical Conditional Neural Process Via Tractable Dependent Predictions. ICLR 2022 - [i66]Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner:
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. CoRR abs/2202.12275 (2022) - [i65]Wessel P. Bruinsma, Martin Tegner, Richard E. Turner:
Modelling Non-Smooth Signals with Complex Spectral Structure. CoRR abs/2203.06997 (2022) - [i64]Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner:
Practical Conditional Neural Processes Via Tractable Dependent Predictions. CoRR abs/2203.08775 (2022) - [i63]Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus:
Multi-disciplinary fairness considerations in machine learning for clinical trials. CoRR abs/2205.08875 (2022) - [i62]Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner:
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. CoRR abs/2206.08671 (2022) - [i61]Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner:
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. CoRR abs/2206.09843 (2022) - [i60]Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Challenges and Pitfalls of Bayesian Unlearning. CoRR abs/2207.03227 (2022) - [i59]Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yao:
The Neural Process Family: Survey, Applications and Perspectives. CoRR abs/2209.00517 (2022) - [i58]Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking:
Kernel Learning for Explainable Climate Science. CoRR abs/2209.04947 (2022) - [i57]Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. CoRR abs/2209.11595 (2022) - [i56]Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser:
Ice Core Dating using Probabilistic Programming. CoRR abs/2210.16568 (2022) - [i55]Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew Lazzara, Daniel C. Jones, J. Scott Hosking, Richard E. Turner:
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement. CoRR abs/2211.10381 (2022) - [i54]Elre T. Oldewage, John Bronskill, Richard E. Turner:
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. CoRR abs/2211.12990 (2022) - 2021
- [c61]Noel Loo, Siddharth Swaroop, Richard E. Turner:
Generalized Variational Continual Learning. ICLR 2021 - [c60]Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner:
How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS 2021: 4093-4105 - [c59]John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner:
Memory Efficient Meta-Learning with Large Images. NeurIPS 2021: 24327-24339 - [c58]Marcin Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E. Turner:
Collapsed Variational Bounds for Bayesian Neural Networks. NeurIPS 2021: 25412-25426 - [c57]Will Tebbutt, Arno Solin, Richard E. Turner:
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes. UAI 2021: 1607-1617 - [i53]Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner:
The Gaussian Neural Process. CoRR abs/2101.03606 (2021) - [i52]Anna Vaughan, William Tebbutt, J. Scott Hosking, Richard E. Turner:
Convolutional conditional neural processes for local climate downscaling. CoRR abs/2101.07950 (2021) - [i51]Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. CoRR abs/2102.06571 (2021) - [i50]Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2104.04034 (2021) - [i49]Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang:
Contextual HyperNetworks for Novel Feature Adaptation. CoRR abs/2104.05860 (2021) - [i48]Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner:
How Tight Can PAC-Bayes be in the Small Data Regime? CoRR abs/2106.03542 (2021) - [i47]Will Tebbutt, Arno Solin, Richard E. Turner:
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes. CoRR abs/2106.10210 (2021) - [i46]Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner:
Continual Novelty Detection. CoRR abs/2106.12964 (2021) - [i45]John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner:
Memory Efficient Meta-Learning with Large Images. CoRR abs/2107.01105 (2021) - [i44]Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Efficient Gaussian Neural Processes for Regression. CoRR abs/2108.09676 (2021) - 2020
- [c56]Jan Stuehmer, Richard E. Turner, Sebastian Nowozin:
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. AISTATS 2020: 1200-1210 - [c55]Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner:
Convolutional Conditional Neural Processes. ICLR 2020 - [c54]Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). ICLR 2020 - [c53]Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner:
Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR 2020 - [c52]John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner:
TaskNorm: Rethinking Batch Normalization for Meta-Learning. ICML 2020: 1153-1164 - [c51]Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner:
Scalable Exact Inference in Multi-Output Gaussian Processes. ICML 2020: 1190-1201 - [c50]Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) 2020: 191-205 - [c49]Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner:
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. NeurIPS 2020 - [c48]Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner:
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS 2020 - [c47]Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS 2020 - [c46]Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS 2020 - [c45]Marcin Tomczak, Siddharth Swaroop, Richard E. Turner:
Efficient Low Rank Gaussian Variational Inference for Neural Networks. NeurIPS 2020 - [i43]John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner:
TaskNorm: Rethinking Batch Normalization for Meta-Learning. CoRR abs/2003.03284 (2020) - [i42]Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR abs/2004.14070 (2020) - [i41]Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR abs/2006.11941 (2020) - [i40]Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner:
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. CoRR abs/2007.01332 (2020) - [i39]Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang:
Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2007.12061 (2020) - [i38]Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman:
Interpreting Spatially Infinite Generative Models. CoRR abs/2007.12411 (2020) - [i37]Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner:
Sparse Gaussian Process Variational Autoencoders. CoRR abs/2010.10177 (2020) - [i36]Noel Loo, Siddharth Swaroop, Richard E. Turner:
Generalized Variational Continual Learning. CoRR abs/2011.12328 (2020)
2010 – 2019
- 2019
- [j9]Abdul-Saboor Sheikh, Nicol S. Harper, Jakob Drefs, Yosef Singer, Zhenwen Dai, Richard E. Turner, Jörg Lücke:
STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds. PLoS Comput. Biol. 15(1) (2019) - [c44]Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI 2019: 1-8 - [c43]Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner:
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. AISTATS 2019: 859-868 - [c42]James Requeima, William Tebbutt, Wessel P. Bruinsma, Richard E. Turner:
The Gaussian Process Autoregressive Regression Model (GPAR). AISTATS 2019: 1860-1869 - [c41]Bo-Hsiang Tseng, Marek Rei, Pawel Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen:
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling. EMNLP/IJCNLP (1) 2019: 1273-1278 - [c40]Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner:
Meta-Learning Probabilistic Inference for Prediction. ICLR (Poster) 2019 - [c39]Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Deterministic Variational Inference for Robust Bayesian Neural Networks. ICLR 2019 - [c38]Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota:
Practical Deep Learning with Bayesian Principles. NeurIPS 2019: 4289-4301 - [c37]James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner:
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes. NeurIPS 2019: 7957-7968 - [c36]Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS 2019: 14791-14802 - [i35]Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner:
Improving and Understanding Variational Continual Learning. CoRR abs/1905.02099 (2019) - [i34]Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore:
Fast computation of loudness using a deep neural network. CoRR abs/1905.10399 (2019) - [i33]Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan:
Practical Deep Learning with Bayesian Principles. CoRR abs/1906.02506 (2019) - [i32]James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner:
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes. CoRR abs/1906.07697 (2019) - [i31]Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
'In-Between' Uncertainty in Bayesian Neural Networks. CoRR abs/1906.11537 (2019) - [i30]Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR abs/1908.04537 (2019) - [i29]Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner:
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR abs/1909.00719 (2019) - [i28]Jan Stühmer, Richard E. Turner, Sebastian Nowozin:
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. CoRR abs/1909.05063 (2019) - [i27]Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner:
Convolutional Conditional Neural Processes. CoRR abs/1910.13556 (2019) - [i26]Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner:
Scalable Exact Inference in Multi-Output Gaussian Processes. CoRR abs/1911.06287 (2019) - [i25]Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). CoRR abs/1911.09514 (2019) - [i24]Mrinank Sharma, Michael Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner:
Differentially Private Federated Variational Inference. CoRR abs/1911.10563 (2019) - [i23]Bo-Hsiang Tseng, Marek Rei, Pawel Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen:
Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling. CoRR abs/1911.11672 (2019) - 2018
- [j8]Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters:
Invariant Models for Causal Transfer Learning. J. Mach. Learn. Res. 19: 36:1-36:34 (2018) - [c35]Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
The Geometry of Random Features. AISTATS 2018: 1-9 - [c34]Yingzhen Li, Richard E. Turner:
Gradient Estimators for Implicit Models. ICLR (Poster) 2018 - [c33]Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) 2018 - [c32]Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner:
Variational Continual Learning. ICLR (Poster) 2018 - [c31]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) 2018 - [c30]Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. ICML 2018: 969-977 - [c29]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML 2018: 5022-5031 - [c28]Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller:
Geometrically Coupled Monte Carlo Sampling. NeurIPS 2018: 195-205 - [c27]Arno Solin, James Hensman, Richard E. Turner:
Infinite-Horizon Gaussian Processes. NeurIPS 2018: 3490-3499 - [i22]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR abs/1802.10031 (2018) - [i21]Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. CoRR abs/1804.02395 (2018) - [i20]Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR abs/1804.11271 (2018) - [i19]Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner:
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. CoRR abs/1805.08651 (2018) - [i18]Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner:
Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning. CoRR abs/1805.09921 (2018) - [i17]Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. CoRR abs/1810.03958 (2018) - [i16]Arno Solin, James Hensman, Richard E. Turner:
Infinite-Horizon Gaussian Processes. CoRR abs/1811.06588 (2018) - [i15]Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner:
Partitioned Variational Inference: A unified framework encompassing federated and continual learning. CoRR abs/1811.11206 (2018) - 2017
- [j7]Thang D. Bui, Josiah Yan, Richard E. Turner:
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. J. Mach. Learn. Res. 18: 104:1-104:72 (2017) - [c26]Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner:
The Multivariate Generalised von Mises Distribution: Inference and Applications. AAAI 2017: 2394-2400 - [c25]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR 2017 - [c24]Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck:
Tuning Recurrent Neural Networks with Reinforcement Learning. ICLR (Workshop) 2017 - [c23]Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck:
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. ICML 2017: 1645-1654 - [c22]Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner:
Magnetic Hamiltonian Monte Carlo. ICML 2017: 3453-3461 - [c21]Thang D. Bui, Cuong V. Nguyen, Richard E. Turner:
Streaming Sparse Gaussian Process Approximations. NIPS 2017: 3299-3307 - [c20]Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS 2017: 3846-3855 - [i14]Yingzhen Li, Richard E. Turner, Qiang Liu:
Approximate Inference with Amortised MCMC. CoRR abs/1702.08343 (2017) - [i13]Yingzhen Li, Richard E. Turner:
Gradient Estimators for Implicit Models. CoRR abs/1705.07107 (2017) - [i12]Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartlomiej Swiatkowski, Bernhard Schölkopf, Richard E. Turner:
Discriminative k-shot learning using probabilistic models. CoRR abs/1706.00326 (2017) - [i11]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR abs/1706.00387 (2017) - [i10]Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner:
Variational Continual Learning. CoRR abs/1710.10628 (2017) - 2016
- [c19]Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani:
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. AISTATS 2016: 231-239 - [c18]Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML 2016: 1472-1481 - [c17]José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. ICML 2016: 1511-1520 - [c16]Felipe A. Tobar, Richard E. Turner:
Modelling time series via automatic learning of basis functions. SAM 2016: 1-5 - [c15]Yingzhen Li, Richard E. Turner:
Rényi Divergence Variational Inference. NIPS 2016: 1073-1081 - [i9]Yingzhen Li, Richard E. Turner:
Variational Inference with Rényi Divergence. CoRR abs/1602.02311 (2016) - [i8]Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR abs/1602.04133 (2016) - [i7]Thang D. Bui, Josiah Yan, Richard E. Turner:
A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation. CoRR abs/1605.07066 (2016) - [i6]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR abs/1611.02247 (2016) - [i5]Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck:
Tuning Recurrent Neural Networks with Reinforcement Learning. CoRR abs/1611.02796 (2016) - 2015
- [j6]Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen:
A multi-label approach to target prediction taking ligand promiscuity into account. J. Cheminformatics 7: 24:1-24:14 (2015) - [c14]Felipe A. Tobar, Richard E. Turner:
Modelling of complex signals using gaussian processes. ICASSP 2015: 2209-2213 - [c13]Yarin Gal, Richard E. Turner:
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. ICML 2015: 655-664 - [c12]Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. NIPS 2015: 2323-2331 - [c11]Shixiang Gu, Zoubin Ghahramani, Richard E. Turner:
Neural Adaptive Sequential Monte Carlo. NIPS 2015: 2629-2637 - [c10]Felipe A. Tobar, Thang D. Bui, Richard E. Turner:
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. NIPS 2015: 3501-3509 - [i4]Shixiang Gu, Zoubin Ghahramani, Richard E. Turner:
Neural Adaptive Sequential Monte Carlo. CoRR abs/1506.03338 (2015) - [i3]Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. CoRR abs/1506.04132 (2015) - [i2]Dan Stowell, Richard E. Turner:
Denoising without access to clean data using a partitioned autoencoder. CoRR abs/1509.05982 (2015) - 2014
- [j5]Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke:
Efficient occlusive components analysis. J. Mach. Learn. Res. 15(1): 2689-2722 (2014) - [j4]