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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
- 2024
[c83]Jonathan So, Richard E. Turner:
Optimising Distributions with Natural Gradient Surrogates. AISTATS 2024: 2224-2232- [c82]Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen:
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. AISTATS 2024: 3331-3339 - [c81]Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Translation Equivariant Transformer Neural Processes. ICML 2024 - [c80]Isabel Chien, Wessel P. Bruinsma, Javier González Hernández, Richard E. Turner:
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. ICML 2024 - [c79]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. ICML 2024 - [c78]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML 2024 - [c77]Lachlan Thorpe, Lewis Bawden, Karanjot Vendal, John Bronskill, Richard E. Turner:
SportsNGEN: Sustained Generation of Realistic Multi-Player Sports Gameplay. icSPORTS 2024: 119-130 - [i101]Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann, Richard E. Turner:
Transformer Neural Autoregressive Flows. CoRR abs/2401.01855 (2024) - [i100]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR abs/2402.03496 (2024) - [i99]Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec:
Denoising Diffusion Probabilistic Models in Six Simple Steps. CoRR abs/2402.04384 (2024) - [i98]James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato:
A Generative Model of Symmetry Transformations. CoRR abs/2403.01946 (2024) - [i97]Lachlan Thorpe, Lewis Bawden, Karanjot Vendal, John Bronskill, Richard E. Turner:
SportsNGEN: Sustained Generation of Multi-player Sports Gameplay. CoRR abs/2403.12977 (2024) - [i96]Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner:
Aardvark Weather: end-to-end data-driven weather forecasting. CoRR abs/2404.00411 (2024) - [i95]James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud:
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR abs/2405.12856 (2024) - [i94]Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris:
Aurora: A Foundation Model of the Atmosphere. CoRR abs/2405.13063 (2024) - [i93]Isaac Reid, Stratis Markou, Krzysztof Choromanski, Richard E. Turner, Adrian Weller:
Variance-Reducing Couplings for Random Features: Perspectives from Optimal Transport. CoRR abs/2405.16541 (2024) - [i92]Jonathan So, Richard E. Turner:
Fearless Stochasticity in Expectation Propagation. CoRR abs/2406.01801 (2024) - [i91]Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner:
Noise-Aware Differentially Private Regression via Meta-Learning. CoRR abs/2406.08569 (2024) - [i90]Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Translation Equivariant Transformer Neural Processes. CoRR abs/2406.12409 (2024) - [i89]Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman:
von Mises Quasi-Processes for Bayesian Circular Regression. CoRR abs/2406.13151 (2024) - [i88]Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner:
Approximately Equivariant Neural Processes. CoRR abs/2406.13488 (2024) - [i87]Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner:
In-Context In-Context Learning with Transformer Neural Processes. CoRR abs/2406.13493 (2024) - [i86]Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner:
Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models. CoRR abs/2407.16526 (2024) - [i85]Anna Vaughan, Gonzalo Mateo-Garcia, Itziar Irakulis-Loitxate, Marc Watine, Pablo Fernandez-Poblaciones, Richard E. Turner, James Requeima, Javier Gorroño, Cynthia Randles, Manfredi Caltagirone, Claudio Cifarelli:
AI for operational methane emitter monitoring from space. CoRR abs/2408.04745 (2024) - [i84]Isaac Reid, Kumar Avinava Dubey, Deepali Jain, Will Whitney, Amr Ahmed, Joshua Ainslie, Alex Bewley, Mithun George Jacob, Aranyak Mehta, David Rendleman, Connor Schenck, Richard E. Turner, René Wagner, Adrian Weller, Krzysztof Choromanski:
Linear Transformer Topological Masking with Graph Random Features. CoRR abs/2410.03462 (2024) - [i83]Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner:
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data. CoRR abs/2410.06731 (2024) - [i82]Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner:
Influence Functions for Scalable Data Attribution in Diffusion Models. CoRR abs/2410.13850 (2024) - [i81]Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu:
On conditional diffusion models for PDE simulations. CoRR abs/2410.16415 (2024) - 2023
- [j12]Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan:
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023) - [j11]Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela:
Differentially private partitioned variational inference. Trans. Mach. Learn. Res. 2023 (2023) - [j10]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. Trans. Mach. Learn. Res. 2023 (2023) - [c76]Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner:
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. CoLLAs 2023: 878-908 - [c75]Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner:
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. ICCV 2023: 18774-18784 - [c74]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 - [c73]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 - [c72]Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig:
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS 2023 - [c71]Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter:
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS 2023 - [c70]Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner:
Geometric Neural Diffusion Processes. NeurIPS 2023 - [c69]Isabel Chien, Javier González Hernández, Richard E. Turner:
Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. TML4H 2023: 51-59 - [i80]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) - [i79]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) - [i78]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) - [i77]Richard E. Turner:
An Introduction to Transformers. CoRR abs/2304.10557 (2023) - [i76]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) - [i75]Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex S. 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) - [i74]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) - [i73]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) - [i72]Jonathan So, Richard E. Turner:
Optimising Distributions with Natural Gradient Surrogates. CoRR abs/2310.11837 (2023) - [i71]Jonas Scholz, Tom R. Andersson, Anna Vaughan, James Requeima, Richard E. Turner:
Sim2Real for Environmental Neural Processes. CoRR abs/2310.19932 (2023) - [i70]Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
:
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR abs/2311.00636 (2023) - [i69]Lorenzo Bonito, James Requeima, Aliaksandra Shysheya, Richard E. Turner:
Diffusion-Augmented Neural Processes. CoRR abs/2311.09848 (2023) - [i68]Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen:
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. CoRR abs/2311.16849 (2023) - [i67]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR abs/2312.05705 (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 J. 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]Richard E. Turner, Maneesh Sahani:
Time-Frequency Analysis as Probabilistic Inference. IEEE Trans. Signal Process. 62(23): 6171-6183 (2014) - [c9]Thang D. Bui, Richard E. Turner:
Tree-structured Gaussian Process Approximations. NIPS 2014: 2213-2221 - [i1]Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen:
Target Fishing: A Single-Label or Multi-Label Problem? CoRR abs/1411.6285 (2014) - 2012
- [c8]Richard E. Turner, Maneesh Sahani:
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. ICASSP 2012: 2173-2176 - 2011
- [j3]Richard E. Turner, Maneesh Sahani:
Demodulation as Probabilistic Inference. IEEE ACM Trans. Audio Speech Lang. Process. 19(8): 2398-2411 (2011) - [c7]Victoria Leong, Richard E. Turner, Michael Stone, Usha Goswami:
Spoken Nursery Rhymes Have a Fractal Rhythmic Structure - Evidence from Patterns of Slow Amplitude Modulation (AM). CogSci 2011 - [c6]Richard E. Turner, Maneesh Sahani:
Probabilistic amplitude and frequency demodulation. NIPS 2011: 981-989 - 2010
- [c5]Richard E. Turner, Maneesh Sahani:
Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. ICASSP 2010: 5466-5469
2000 – 2009
- 2009
- [j2]Pietro Berkes, Richard E. Turner, Maneesh Sahani:
A Structured Model of Video Reproduces Primary Visual Cortical Organisation. PLoS Comput. Biol. 5(9) (2009) - [c4]Jörg Lücke, Richard E. Turner, Maneesh Sahani, Marc Henniges:
Occlusive Components Analysis. NIPS 2009: 1069-1077 - 2007
- [j1]Richard E. Turner, Maneesh Sahani:
A Maximum-Likelihood Interpretation for Slow Feature Analysis. Neural Comput. 19(4): 1022-1038 (2007) - [c3]Richard E. Turner, Maneesh Sahani:
Probabilistic Amplitude Demodulation. ICA 2007: 544-551 - [c2]Pietro Berkes, Richard E. Turner, Maneesh Sahani:
On Sparsity and Overcompleteness in Image Models. NIPS 2007: 89-96 - [c1]Richard E. Turner, Maneesh Sahani:
Modeling Natural Sounds with Modulation Cascade Processes. NIPS 2007: 1545-1552
Coauthor Index
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