Richard E. Turner

234 papers A* 50A 10B 1Misc 3Journal 136Unranked 34

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Error Propagation and Model Collapse in Diffusion Models: A Theoretical Study. CoRR Nail B. Khelifa, Richard E. Turner, Ramji Venkataramanan
2026	J	`jnl`	Incremental Transformer Neural Processes. CoRR Philip Mortimer, Cristiana Diaconu, Tommy Rochussen, Bruno Mlodozeniec, Richard E. Turner
2026	J	`jnl`	Probabilistic Retrofitting of Learned Simulators. CoRR Cristiana Diaconu, Miles D. Cranmer, Richard E. Turner, Tanya Marwah, Payel Mukhopadhyay
2025	J	`jnl`	A Fourier Space Perspective on Diffusion Models. CoRR Fabian Falck, Teodora Pandeva, Kiarash Zahirnia, Rachel Lawrence, Richard E. Turner, Edward Meeds, Javier Zazo, Sushrut Karmalkar
2025	J	`jnl`	A foundation model for the Earth system. Nat. Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Chun-Chieh Wu, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
2025	J	`jnl`	Artificial intelligence for methane detection: from continuous monitoring to verified mitigation. CoRR Anna Allen, Gonzalo Mateo-Garcia, Itziar Irakulis-Loitxate, Manuel Montesino San Martín, Marc Watine, James Requeima, Javier Gorroño, Cynthia Randles, Tharwat Mokalled, Luis Guanter, Richard E. Turner, Claudio Cifarelli, Manfredi Caltagirone
2025	A	`conf`	Bayesian Circular Regression with von Mises Quasi-Processes. AISTATS Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman
2025	J	`jnl`	Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions. CoRR Steve Hong, Runa Eschenhagen, Bruno Mlodozeniec, Richard E. Turner
2025	J	`jnl`	Distributional Training Data Attribution. CoRR Bruno Mlodozeniec, Isaac Reid, Sam Power, David Krueger, Murat A. Erdogdu, Richard E. Turner, Roger B. Grosse
2025	J	`jnl`	Efficient Few-Shot Continual Learning in Vision-Language Models. CoRR Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner
2025	J	`jnl`	Efficient Few-Shot Continual Learning in Vision-Language Models. Trans. Mach. Learn. Res. Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner
2025	J	`jnl`	End-to-end data-driven weather prediction. Nat. Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking, Richard E. Turner
2025	J	`jnl`	Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning. CoRR Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu, James Requeima, Richard E. Turner, Mark van der Wilk
2025	J	`jnl`	Fresh in memory: Training-order recency is linearly encoded in language model activations. CoRR Dmitrii Krasheninnikov, Richard E. Turner, David Krueger
2025	J	`jnl`	Graph Random Features for Scalable Gaussian Processes. CoRR Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski, Adrian Weller, Richard E. Turner, Isaac Reid
2025	A*	`conf`	Gridded Transformer Neural Processes for Spatio-Temporal Data. ICML Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner
2025	A*	`conf`	Influence Functions for Scalable Data Attribution in Diffusion Models. ICLR Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner
2025	J	`jnl`	JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs. CoRR Aliaksandra Shysheya, John Bronskill, James Requeima, Shoaib Ahmed Siddiqui, Javier González, David Duvenaud, Richard E. Turner
2025	A*	`conf`	Linear Transformer Topological Masking with Graph Random Features. ICLR Isaac Reid, Kumar Avinava Dubey, Deepali Jain, William F. Whitney, Amr Ahmed, Joshua Ainslie, Alex Bewley, Mithun George Jacob, Aranyak Mehta, David Rendleman, Connor Schenck, Richard E. Turner, René Wagner, Adrian Weller, Krzysztof Marcin Choromanski
2025	—	`conf`	Position: Probabilistic Modelling is Sufficient for Causal Inference. ICML (Position Papers) Bruno Kacper Mlodozeniec, David Krueger, Richard E. Turner
2025	J	`jnl`	Probabilistic Modelling is Sufficient for Causal Inference. CoRR Bruno Mlodozeniec, David Krueger, Richard E. Turner
2025	J	`jnl`	Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner. CoRR Runa Eschenhagen, Aaron Defazio, Tsung-Hsien Lee, Richard E. Turner, Hao-Jun Michael Shi
2025	J	`jnl`	Refined climatologies of future precipitation over High Mountain Asia using probabilistic ensemble learning. CoRR Kenza Tazi, Sun Woo P. Kim, Marc Girona-Mata, Richard E. Turner
2025	J	`jnl`	Spectral-factorized Positive-definite Curvature Learning for NN Training. CoRR Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Roger B. Grosse
2025	J	`jnl`	Tighter sparse variational Gaussian processes. CoRR Thang D. Bui, Matthew Ashman, Richard E. Turner
2025	J	`jnl`	Tighter sparse variational Gaussian processes. Trans. Mach. Learn. Res. Thang D. Bui, Matthew Ashman, Richard E. Turner
2025	A*	`conf`	Variance-Reducing Couplings for Random Features. ICLR Isaac Reid, Stratis Markou, Krzysztof Marcin Choromanski, Richard E. Turner, Adrian Weller
2025	J	`jnl`	Warm Starts Accelerate Generative Modelling. CoRR Jonas Scholz, Richard E. Turner
2025	J	`jnl`	Wavelet-Induced Rotary Encodings: RoPE Meets Graphs. CoRR Isaac Reid, Arijit Sehanobish, Cederik Höfs, Bruno Mlodozeniec, Leonhard Vulpius, Federico Barbero, Adrian Weller, Krzysztof Choromanski, Richard E. Turner, Petar Velickovic
2024	A*	`conf`	A Generative Model of Symmetry Transformations. NeurIPS James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	J	`jnl`	A Generative Model of Symmetry Transformations. CoRR James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	J	`jnl`	AI for operational methane emitter monitoring from space. CoRR 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
2024	J	`jnl`	Aardvark Weather: end-to-end data-driven weather forecasting. CoRR 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
2024	A*	`conf`	Approximately Equivariant Neural Processes. NeurIPS Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Approximately Equivariant Neural Processes. CoRR Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Aurora: A Foundation Model of the Atmosphere. CoRR 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, Alexander T. Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
2024	—	`conf`	Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects. MLHC Isabel Chien, Cliff Wong, Zelalem Gero, Jaspreet Bagga, Risa Ueno, Richard E. Turner, Roshanthi Weerasinghe, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon, Javier González Hernández
2024	A*	`conf`	Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. ICML Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani
2024	J	`jnl`	Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani
2024	J	`jnl`	Denoising Diffusion Probabilistic Models in Six Simple Steps. CoRR Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya, Andrew Y. K. Foong, Bruno Mlodozeniec
2024	A*	`conf`	Fearless Stochasticity in Expectation Propagation. NeurIPS Jonathan So, Richard E. Turner
2024	J	`jnl`	Fearless Stochasticity in Expectation Propagation. CoRR Jonathan So, Richard E. Turner
2024	J	`jnl`	Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data. CoRR Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner
2024	A	`conf`	Identifiable Feature Learning for Spatial Data with Nonlinear ICA. AISTATS Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
2024	J	`jnl`	Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models. CoRR Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner
2024	—	`conf`	Implicitly Bayesian Prediction Rules in Deep Learning. AABI Bruno Mlodozeniec, David Krueger, Richard E. Turner
2024	—	`conf`	In-Context In-Context Learning with Transformer Neural Processes. AABI Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner
2024	J	`jnl`	In-Context In-Context Learning with Transformer Neural Processes. CoRR Matthew Ashman, Cristiana Diaconu, Adrian Weller, Richard E. Turner
2024	J	`jnl`	Influence Functions for Scalable Data Attribution in Diffusion Models. CoRR Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner
2024	A*	`conf`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. NeurIPS James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Kristjanson Duvenaud
2024	J	`jnl`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
2024	J	`jnl`	Linear Transformer Topological Masking with Graph Random Features. CoRR 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
2024	A*	`conf`	Noise-Aware Differentially Private Regression via Meta-Learning. NeurIPS Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
2024	J	`jnl`	Noise-Aware Differentially Private Regression via Meta-Learning. CoRR Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner
2024	A*	`conf`	On conditional diffusion models for PDE simulations. NeurIPS Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu
2024	J	`jnl`	On conditional diffusion models for PDE simulations. CoRR Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu
2024	A	`conf`	Optimising Distributions with Natural Gradient Surrogates. AISTATS Jonathan So, Richard E. Turner
2024	A*	`conf`	Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. ICML Isabel Chien, Wessel P. Bruinsma, Javier González Hernández, Richard E. Turner
2024	J	`jnl`	SportsNGEN: Sustained Generation of Multi-player Sports Gameplay. CoRR Lachlan Thorpe, Lewis Bawden, Karanjot Vendal, John Bronskill, Richard E. Turner
2024	—	`conf`	SportsNGEN: Sustained Generation of Realistic Multi-Player Sports Gameplay. icSPORTS Lachlan Thorpe, Lewis Bawden, Karanjot Vendal, John Bronskill, Richard E. Turner
2024	A*	`conf`	Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2024	J	`jnl`	Transformer Neural Autoregressive Flows. CoRR Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann, Richard E. Turner
2024	A*	`conf`	Translation Equivariant Transformer Neural Processes. ICML Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Translation Equivariant Transformer Neural Processes. CoRR Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2024	J	`jnl`	Variance-Reducing Couplings for Random Features: Perspectives from Optimal Transport. CoRR Isaac Reid, Stratis Markou, Krzysztof Choromanski, Richard E. Turner, Adrian Weller
2024	J	`jnl`	von Mises Quasi-Processes for Bayesian Circular Regression. CoRR Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman
2023	J	`jnl`	An Introduction to Transformers. CoRR Richard E. Turner
2023	A*	`conf`	Autoregressive Conditional Neural Processes. ICLR Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
2023	J	`jnl`	Autoregressive Conditional Neural Processes. CoRR Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner
2023	J	`jnl`	Beyond Intuition, a Framework for Applying GPs to Real-World Data. CoRR Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex S. Gardner, S. T. John, Hong Ge, Richard E. Turner
2023	—	`conf`	Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. CoLLAs Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner
2023	J	`jnl`	Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation. CoRR Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner
2023	J	`jnl`	Differentially private partitioned variational inference. Trans. Mach. Learn. Res. Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
2023	J	`jnl`	Diffusion-Augmented Neural Processes. CoRR Lorenzo Bonito, James Requeima, Aliaksandra Shysheya, Richard E. Turner
2023	A*	`conf`	FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. ICLR Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner
2023	A*	`conf`	First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. ICCV Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner
2023	J	`jnl`	First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning. CoRR Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino, Rahaf Aljundi, Richard E. Turner
2023	A*	`conf`	Geometric Neural Diffusion Processes. NeurIPS Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
2023	J	`jnl`	Geometric Neural Diffusion Processes. CoRR Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
2023	J	`jnl`	Identifiable Feature Learning for Spatial Data with Nonlinear ICA. CoRR Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
2023	J	`jnl`	Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
2023	A*	`conf`	Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
2023	J	`jnl`	Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
2023	J	`jnl`	On the Efficacy of Differentially Private Few-shot Image Classification. CoRR Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Béguelin, Richard E. Turner, Antti Honkela
2023	J	`jnl`	On the Efficacy of Differentially Private Few-shot Image Classification. Trans. Mach. Learn. Res. Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella-Béguelin, Richard E. Turner, Antti Honkela
2023	J	`jnl`	Optimising Distributions with Natural Gradient Surrogates. CoRR Jonathan So, Richard E. Turner
2023	A*	`conf`	PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
2023	J	`jnl`	PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
2023	—	`conf`	Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. TML4H Isabel Chien, Javier González Hernández, Richard E. Turner
2023	J	`jnl`	Sim2Real for Environmental Neural Processes. CoRR Jonas Scholz, Tom R. Andersson, Anna Vaughan, James Requeima, Richard E. Turner
2023	J	`jnl`	Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2022	J	`jnl`	Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement. CoRR 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
2022	—	`conf`	Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. ICBINB Elre T. Oldewage, John Bronskill, Richard E. Turner
2022	J	`jnl`	Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners. CoRR Elre T. Oldewage, John Bronskill, Richard E. Turner
2022	A*	`conf`	Bayesian Neural Network Priors Revisited. ICLR Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2022	J	`jnl`	Challenges and Pitfalls of Bayesian Unlearning. CoRR Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2022	A*	`conf`	Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. NeurIPS Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
2022	J	`jnl`	Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. CoRR Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
2022	—	`conf`	Continual Novelty Detection. CoLLAs Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner
2022	J	`jnl`	Differentially private partitioned variational inference. CoRR Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela
2022	J	`jnl`	FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. CoRR Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner
2022	J	`jnl`	Ice Core Dating using Probabilistic Programming. CoRR Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
2022	J	`jnl`	Kernel Learning for Explainable Climate Science. CoRR Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking
2022	A	`conf`	Modelling Non-Smooth Signals with Complex Spectral Structure. AISTATS Wessel P. Bruinsma, Martin Tegner, Richard E. Turner
2022	J	`jnl`	Modelling Non-Smooth Signals with Complex Spectral Structure. CoRR Wessel P. Bruinsma, Martin Tegner, Richard E. Turner
2022	—	`conf`	Multi-disciplinary fairness considerations in machine learning for clinical trials. FAccT Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus
2022	J	`jnl`	Multi-disciplinary fairness considerations in machine learning for clinical trials. CoRR Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus
2022	J	`jnl`	Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. CoRR Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner
2022	A*	`conf`	Practical Conditional Neural Process Via Tractable Dependent Predictions. ICLR Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
2022	J	`jnl`	Practical Conditional Neural Processes Via Tractable Dependent Predictions. CoRR Stratis Markou, James Requeima, Wessel P. Bruinsma, Anna Vaughan, Richard E. Turner
2022	J	`jnl`	The Neural Process Family: Survey, Applications and Perspectives. CoRR Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yao
2021	J	`jnl`	Bayesian Neural Network Priors Revisited. CoRR Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2021	A*	`conf`	Collapsed Variational Bounds for Bayesian Neural Networks. NeurIPS Marcin Tomczak, Siddharth Swaroop, Andrew Y. K. Foong, Richard E. Turner
2021	J	`jnl`	Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes. CoRR Will Tebbutt, Arno Solin, Richard E. Turner
2021	A	`conf`	Combining pseudo-point and state space approximations for sum-separable Gaussian Processes. UAI Will Tebbutt, Arno Solin, Richard E. Turner
2021	J	`jnl`	Contextual HyperNetworks for Novel Feature Adaptation. CoRR Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
2021	J	`jnl`	Continual Novelty Detection. CoRR Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner
2021	J	`jnl`	Convolutional conditional neural processes for local climate downscaling. CoRR Anna Vaughan, William Tebbutt, J. Scott Hosking, Richard E. Turner
2021	J	`jnl`	Efficient Gaussian Neural Processes for Regression. CoRR Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2021	A*	`conf`	Generalized Variational Continual Learning. ICLR Noel Loo, Siddharth Swaroop, Richard E. Turner
2021	A*	`conf`	How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
2021	J	`jnl`	How Tight Can PAC-Bayes be in the Small Data Regime? CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
2021	A*	`conf`	Memory Efficient Meta-Learning with Large Images. NeurIPS John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
2021	J	`jnl`	Memory Efficient Meta-Learning with Large Images. CoRR John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner
2021	J	`jnl`	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR 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
2021	J	`jnl`	The Gaussian Neural Process. CoRR Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner
2020	A*	`conf`	Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner
2020	A*	`conf`	Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
2020	J	`jnl`	Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
2020	A*	`conf`	Continual Learning with Adaptive Weights (CLAW). ICLR Tameem Adel, Han Zhao, Richard E. Turner
2020	A*	`conf`	Convolutional Conditional Neural Processes. ICLR Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2020	J	`jnl`	Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR 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
2020	A*	`conf`	Efficient Low Rank Gaussian Variational Inference for Neural Networks. NeurIPS Marcin Tomczak, Siddharth Swaroop, Richard E. Turner
2020	J	`jnl`	Generalized Variational Continual Learning. CoRR Noel Loo, Siddharth Swaroop, Richard E. Turner
2020	A	`conf`	Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. AISTATS Jan Stuehmer, Richard E. Turner, Sebastian Nowozin
2020	J	`jnl`	Interpreting Spatially Infinite Generative Models. CoRR Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman
2020	A*	`conf`	Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. NeurIPS Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
2020	J	`jnl`	Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner
2020	A*	`conf`	On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
2020	—	`conf`	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) 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
2020	A*	`conf`	Scalable Exact Inference in Multi-Output Gaussian Processes. ICML Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner
2020	J	`jnl`	Sparse Gaussian Process Variational Autoencoders. CoRR Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
2020	A*	`conf`	TaskNorm: Rethinking Batch Normalization for Meta-Learning. ICML John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner
2020	J	`jnl`	TaskNorm: Rethinking Batch Normalization for Meta-Learning. CoRR John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner
2020	A*	`conf`	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2020	J	`jnl`	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang
2019	J	`jnl`	'In-Between' Uncertainty in Bayesian Neural Networks. CoRR Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2019	J	`jnl`	Continual Learning with Adaptive Weights (CLAW). CoRR Tameem Adel, Han Zhao, Richard E. Turner
2019	J	`jnl`	Convolutional Conditional Neural Processes. CoRR Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019	—	`conf`	Deterministic Variational Inference for Robust Bayesian Neural Networks. ICLR Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
2019	J	`jnl`	Differentially Private Federated Variational Inference. CoRR Mrinank Sharma, Michael J. Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner
2019	J	`jnl`	Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes. CoRR James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner
2019	A*	`conf`	Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes. NeurIPS James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner
2019	J	`jnl`	Fast computation of loudness using a deep neural network. CoRR Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore
2019	—	`conf`	HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
2019	A*	`conf`	Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Improving and Understanding Variational Continual Learning. CoRR Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner
2019	J	`jnl`	Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. CoRR Jan Stühmer, Richard E. Turner, Sebastian Nowozin
2019	—	`conf`	Meta-Learning Probabilistic Inference for Prediction. ICLR (Poster) Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner
2019	A	`conf`	Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. AISTATS Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner
2019	J	`jnl`	Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
2019	A*	`conf`	Practical Deep Learning with Bayesian Principles. NeurIPS Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
2019	J	`jnl`	Practical Deep Learning with Bayesian Principles. CoRR Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019	J	`jnl`	STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds. PLoS Comput. Biol. Abdul-Saboor Sheikh, Nicol S. Harper, Jakob Drefs, Yosef Singer, Zhenwen Dai, Richard E. Turner, Jörg Lücke
2019	J	`jnl`	Scalable Exact Inference in Multi-Output Gaussian Processes. CoRR Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner
2019	—	`conf`	Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling. EMNLP/IJCNLP (1) Bo-Hsiang Tseng, Marek Rei, Pawel Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen
2019	J	`jnl`	Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling. CoRR Bo-Hsiang Tseng, Marek Rei, Pawel Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen
2019	A	`conf`	The Gaussian Process Autoregressive Regression Model (GPAR). AISTATS James Requeima, William Tebbutt, Wessel P. Bruinsma, Richard E. Turner
2018	J	`jnl`	Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning. CoRR Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner
2018	J	`jnl`	Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. CoRR Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
2018	—	`conf`	Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani
2018	J	`jnl`	Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018	A*	`conf`	Geometrically Coupled Monte Carlo Sampling. NeurIPS Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller
2018	—	`conf`	Gradient Estimators for Implicit Models. ICLR (Poster) Yingzhen Li, Richard E. Turner
2018	A*	`conf`	Infinite-Horizon Gaussian Processes. NeurIPS Arno Solin, James Hensman, Richard E. Turner
2018	J	`jnl`	Infinite-Horizon Gaussian Processes. CoRR Arno Solin, James Hensman, Richard E. Turner
2018	J	`jnl`	Invariant Models for Causal Transfer Learning. J. Mach. Learn. Res. Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters
2018	J	`jnl`	Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. CoRR Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner
2018	J	`jnl`	Partitioned Variational Inference: A unified framework encompassing federated and continual learning. CoRR Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner
2018	A*	`conf`	Structured Evolution with Compact Architectures for Scalable Policy Optimization. ICML Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
2018	J	`jnl`	Structured Evolution with Compact Architectures for Scalable Policy Optimization. CoRR Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
2018	A	`conf`	The Geometry of Random Features. AISTATS Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller
2018	—	`conf`	The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
2018	A*	`conf`	The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
2018	J	`jnl`	The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
2018	—	`conf`	Variational Continual Learning. ICLR (Poster) Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
2017	J	`jnl`	A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. J. Mach. Learn. Res. Thang D. Bui, Josiah Yan, Richard E. Turner
2017	J	`jnl`	Approximate Inference with Amortised MCMC. CoRR Yingzhen Li, Richard E. Turner, Qiang Liu
2017	J	`jnl`	Discriminative k-shot learning using probabilistic models. CoRR Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartlomiej Swiatkowski, Bernhard Schölkopf, Richard E. Turner
2017	J	`jnl`	Gradient Estimators for Implicit Models. CoRR Yingzhen Li, Richard E. Turner
2017	—	`conf`	Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine
2017	J	`jnl`	Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine
2017	A*	`conf`	Magnetic Hamiltonian Monte Carlo. ICML Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner
2017	—	`conf`	Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
2017	A*	`conf`	Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. ICML Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck
2017	—	`conf`	Streaming Sparse Gaussian Process Approximations. NIPS Thang D. Bui, Cuong V. Nguyen, Richard E. Turner
2017	A*	`conf`	The Multivariate Generalised von Mises Distribution: Inference and Applications. AAAI Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner
2017	—	`conf`	Tuning Recurrent Neural Networks with Reinforcement Learning. ICLR (Workshop) Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
2017	J	`jnl`	Variational Continual Learning. CoRR Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
2016	J	`jnl`	A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation. CoRR Thang D. Bui, Josiah Yan, Richard E. Turner
2016	A*	`conf`	Black-Box Alpha Divergence Minimization. ICML José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
2016	A*	`conf`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner
2016	J	`jnl`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2016	—	`conf`	Modelling time series via automatic learning of basis functions. SAM Felipe A. Tobar, Richard E. Turner
2016	A	`conf`	On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. AISTATS Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani
2016	J	`jnl`	Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
2016	—	`conf`	Rényi Divergence Variational Inference. NIPS Yingzhen Li, Richard E. Turner
2016	J	`jnl`	Tuning Recurrent Neural Networks with Reinforcement Learning. CoRR Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
2016	J	`jnl`	Variational Inference with Rényi Divergence. CoRR Yingzhen Li, Richard E. Turner
2015	J	`jnl`	A multi-label approach to target prediction taking ligand promiscuity into account. J. Cheminformatics Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen
2015	J	`jnl`	Denoising without access to clean data using a partitioned autoencoder. CoRR Dan Stowell, Richard E. Turner
2015	A*	`conf`	Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. ICML Yarin Gal, Richard E. Turner
2015	—	`conf`	Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. NIPS Felipe A. Tobar, Thang D. Bui, Richard E. Turner
2015	Misc	`conf`	Modelling of complex signals using gaussian processes. ICASSP Felipe A. Tobar, Richard E. Turner
2015	—	`conf`	Neural Adaptive Sequential Monte Carlo. NIPS Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
2015	J	`jnl`	Neural Adaptive Sequential Monte Carlo. CoRR Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
2015	—	`conf`	Stochastic Expectation Propagation. NIPS Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2015	J	`jnl`	Stochastic Expectation Propagation. CoRR Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2014	J	`jnl`	Efficient occlusive components analysis. J. Mach. Learn. Res. Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke
2014	J	`jnl`	Target Fishing: A Single-Label or Multi-Label Problem? CoRR Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen
2014	J	`jnl`	Time-Frequency Analysis as Probabilistic Inference. IEEE Trans. Signal Process. Richard E. Turner, Maneesh Sahani
2014	—	`conf`	Tree-structured Gaussian Process Approximations. NIPS Thang D. Bui, Richard E. Turner
2012	Misc	`conf`	Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. ICASSP Richard E. Turner, Maneesh Sahani
2011	J	`jnl`	Demodulation as Probabilistic Inference. IEEE ACM Trans. Audio Speech Lang. Process. Richard E. Turner, Maneesh Sahani
2011	—	`conf`	Probabilistic amplitude and frequency demodulation. NIPS Richard E. Turner, Maneesh Sahani
2011	B	`conf`	Spoken Nursery Rhymes Have a Fractal Rhythmic Structure - Evidence from Patterns of Slow Amplitude Modulation (AM). CogSci Victoria Leong, Richard E. Turner, Michael Stone, Usha Goswami
2010	Misc	`conf`	Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. ICASSP Richard E. Turner, Maneesh Sahani
2009	J	`jnl`	A Structured Model of Video Reproduces Primary Visual Cortical Organisation. PLoS Comput. Biol. Pietro Berkes, Richard E. Turner, Maneesh Sahani
2009	—	`conf`	Occlusive Components Analysis. NIPS Jörg Lücke, Richard E. Turner, Maneesh Sahani, Marc Henniges
2007	J	`jnl`	A Maximum-Likelihood Interpretation for Slow Feature Analysis. Neural Comput. Richard E. Turner, Maneesh Sahani
2007	—	`conf`	Modeling Natural Sounds with Modulation Cascade Processes. NIPS Richard E. Turner, Maneesh Sahani
2007	—	`conf`	On Sparsity and Overcompleteness in Image Models. NIPS Pietro Berkes, Richard E. Turner, Maneesh Sahani
2007	—	`conf`	Probabilistic Amplitude Demodulation. ICA Richard E. Turner, Maneesh Sahani