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ORCIDRichard E. Turner
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- affiliation: University of Cambridge, Department of Engineering, UK
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2020 – today
- 2023
[j10]Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023)- [c71]Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner:
Autoregressive Conditional Neural Processes. ICLR 2023 - [c70]Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner:
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. ICLR 2023 - [c69]Isabel Chien, Javier González Hernández, Richard E. Turner:
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. TML4H 2023: 51-59 - [i74]Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, Antti Honkela:
On the Efficacy of Differentially Private Few-shot Image Classification. CoRR abs/2302.01190 (2023) - [i73]Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner:
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. CoRR abs/2303.13199 (2023) - [i72]Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner:
Autoregressive Conditional Neural Processes. CoRR abs/2303.14468 (2023) - [i71]Richard E. Turner:
An Introduction to Transformers. CoRR abs/2304.10557 (2023) - [i70]Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner:
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. CoRR abs/2306.13554 (2023) - [i69]Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, S. T. John, Hong Ge, Richard E. Turner:
Beyond Intuition, a Framework for Applying GPs to Real-World Data. CoRR abs/2307.03093 (2023) - [i68]Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner:
Geometric Neural Diffusion Processes. CoRR abs/2307.05431 (2023) - [i67]Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter:
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR abs/2308.05732 (2023) - 2022
- [c68]Wessel P. Bruinsma, Martin Tegner, Richard E. Turner:
Modelling Non-Smooth Signals with Complex Spectral Structure. AISTATS 2022: 5166-5195 - [c67]Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner:
Continual Novelty Detection. CoLLAs 2022: 1004-1025 - [c66]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 - [c65]Elre T. Oldewage, John Bronskill, Richard E. Turner:
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. ICBINB 2022: 27-40 - [c64]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 - [c63]Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner:
Practical Conditional Neural Process Via Tractable Dependent Predictions. ICLR 2022 - [c62]Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner:
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. NeurIPS 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</