Mark van der Wilk : Publications

A Bayesian Perspective on Training Speed and Model Selection

Clare Lyle‚ Lisa Schut‚ Robin Ru‚ Yarin Gal and Mark van der Wilk

In H. Larochelle‚ M. Ranzato‚ R. Hadsell‚ M. F. Balcan and H. Lin, editors, Advances in Neural Information Processing Systems (NeurIPS). Vol. 33. Pages 10396–10408. Curran Associates‚ Inc.. 2020.

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A Framework for Interdomain and Multioutput Gaussian Processes

Mark van der Wilk‚ Vincent Dutordoir‚ ST John‚ Artem Artemev‚ Vincent Adam and James Hensman

2020.

Details about A Framework for Interdomain and Multioutput Gaussian Processes | BibTeX data for A Framework for Interdomain and Multioutput Gaussian Processes | Link to A Framework for Interdomain and Multioutput Gaussian Processes

Actually Sparse Variational Gaussian Processes

Harry Jake Cunningham‚ Daniel Augusto de Souza‚ So Takao‚ Mark van der Wilk and Marc Peter Deisenroth

In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. 2023.

Details about Actually Sparse Variational Gaussian Processes | BibTeX data for Actually Sparse Variational Gaussian Processes | Link to Actually Sparse Variational Gaussian Processes

Barely Biased Learning for Gaussian Process Regression

David R. Burt‚ Artem Artemev and Mark van der Wilk

2021.

Details about Barely Biased Learning for Gaussian Process Regression | BibTeX data for Barely Biased Learning for Gaussian Process Regression | Link to Barely Biased Learning for Gaussian Process Regression

Bayesian Image Classification with Deep Convolutional Gaussian Processes

Vincent Dutordoir‚ Mark van der Wilk‚ Artem Artemev and James Hensman

In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS). Vol. 108 of Proceedings of Machine Learning Research. Pages 1529–1539. PMLR. August, 2020.

Details about Bayesian Image Classification with Deep Convolutional Gaussian Processes | BibTeX data for Bayesian Image Classification with Deep Convolutional Gaussian Processes | Link to Bayesian Image Classification with Deep Convolutional Gaussian Processes

Bayesian Layers: A Module for Neural Network Uncertainty

Dustin Tran‚ Mike Dusenberry‚ Mark van der Wilk and Danijar Hafner

In H. Wallach‚ H. Larochelle‚ A. Beygelzimer‚ F. Alché−Buc‚ E. Fox and R. Garnett, editors, Advances in Neural Information Processing Systems 32 (NeurIPS). Vol. 32. Curran Associates‚ Inc.. 2019.

Details about Bayesian Layers: A Module for Neural Network Uncertainty | BibTeX data for Bayesian Layers: A Module for Neural Network Uncertainty | Download (pdf) of Bayesian Layers: A Module for Neural Network Uncertainty

Bayesian Neural Network Priors Revisited

Vincent Fortuin‚ Adrià Garriga−Alonso‚ Sebastian W. Ober‚ Florian Wenzel‚ Gunnar Ratsch‚ Richard E Turner‚ Mark van der Wilk and Laurence Aitchison

In International Conference on Learning Representations (ICLR). 2022.

Details about Bayesian Neural Network Priors Revisited | BibTeX data for Bayesian Neural Network Priors Revisited | Link to Bayesian Neural Network Priors Revisited

Capsule Networks – A Probabilistic Perspective

Lewis Smith‚ Lisa Schut‚ Yarin Gal and Mark van der Wilk

2020.

Details about Capsule Networks – A Probabilistic Perspective | BibTeX data for Capsule Networks – A Probabilistic Perspective | Link to Capsule Networks – A Probabilistic Perspective

Causal Discovery using Bayesian Model Selection

Anish Dhir and Mark van der Wilk

2023.

Details about Causal Discovery using Bayesian Model Selection | BibTeX data for Causal Discovery using Bayesian Model Selection

Causal Discovery using Marginal Likelihood

Anish Dhir and Mark van der Wilk

In NeurIPS Workshop on Causality for Real−world Impact. 2022.

Details about Causal Discovery using Marginal Likelihood | BibTeX data for Causal Discovery using Marginal Likelihood | Link to Causal Discovery using Marginal Likelihood

Closed−form Inference and Prediction in Gaussian Process State−Space Models

Alessandro Davide Ialongo‚ Mark van der Wilk and Carl Edward Rasmussen

In NIPS 2017 Workshop on Time Series. 2017.

Details about Closed−form Inference and Prediction in Gaussian Process State−Space Models | BibTeX data for Closed−form Inference and Prediction in Gaussian Process State−Space Models | Link to Closed−form Inference and Prediction in Gaussian Process State−Space Models

Combining multi−fidelity modelling and asynchronous batch Bayesian Optimization

Jose Pablo Folch‚ Robert M. Lee‚ Behrang Shafei‚ David Walz‚ Calvin Tsay‚ Mark van der Wilk and Ruth Misener

In Computers & Chemical Engineering. Vol. 172. 2023.

Details about Combining multi−fidelity modelling and asynchronous batch Bayesian Optimization | BibTeX data for Combining multi−fidelity modelling and asynchronous batch Bayesian Optimization | DOI (https://doi.org/10.1016/j.compchemeng.2023.108194) | Link to Combining multi−fidelity modelling and asynchronous batch Bayesian Optimization

Competitive Amplification Networks enable molecular pattern recognition with PCR

John P Goertz‚ Ruby Sedgwick‚ Francesca Smith‚ Myrsini Kaforou‚ Victoria J Wright‚ Jethro A. Herberg‚ Zsofia Kote−Jarai‚ Ros Eeles‚ Mike Levin‚ Ruth Misener‚ Mark van der Wilk and Molly M Stevens

In bioRxiv. 2023.

Details about Competitive Amplification Networks enable molecular pattern recognition with PCR | BibTeX data for Competitive Amplification Networks enable molecular pattern recognition with PCR | DOI (10.1101/2023.06.29.546934) | Link to Competitive Amplification Networks enable molecular pattern recognition with PCR

Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning

Rowan McAllister‚ Yarin Gal‚ Alex Kendall‚ Mark van der Wilk‚ Amar Shah‚ Roberto Cipolla and Adrian Vivian Weller

International Joint Conferences on Artificial Intelligence. 2017.

Details about Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning | BibTeX data for Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning | Download (pdf) of Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning

Convergence of Sparse Variational Inference in Gaussian Processes Regression

David R. Burt‚ Carl Edward Rasmussen and Mark van der Wilk

In Journal of Machine Learning Research. Vol. 21. No. 131. Pages 1−63. 2020.

Details about Convergence of Sparse Variational Inference in Gaussian Processes Regression | BibTeX data for Convergence of Sparse Variational Inference in Gaussian Processes Regression | Link to Convergence of Sparse Variational Inference in Gaussian Processes Regression

Convolutional Gaussian Processes

Mark van der Wilk‚ Carl Edward Rasmussen and James Hensman

In I. Guyon‚ U. V. Luxburg‚ S. Bengio‚ H. Wallach‚ R. Fergus‚ S. Vishwanathan and R. Garnett, editors, Advances in Neural Information Processing Systems 30 (NIPS). Pages 2849–2858. Curran Associates‚ Inc.. 2017.

Details about Convolutional Gaussian Processes | BibTeX data for Convolutional Gaussian Processes | Download (pdf) of Convolutional Gaussian Processes

Correlated weights in infinite limits of deep convolutional neural networks

Adrià Garriga−Alonso and Mark van der Wilk

In Cassio de Campos and Marloes H. Maathuis, editors, Proceedings of the Thirty−Seventh Conference on Uncertainty in Artificial Intelligence. Vol. 161 of Proceedings of Machine Learning Research. Pages 1998–2007. PMLR. July, 2021.

Details about Correlated weights in infinite limits of deep convolutional neural networks | BibTeX data for Correlated weights in infinite limits of deep convolutional neural networks | Link to Correlated weights in infinite limits of deep convolutional neural networks

Current Methods for Drug Property Prediction in the Real World

Jacob Green‚ Cecilia Cabrera Diaz‚ Maximilian A. H. Jakobs‚ Andrea Dimitracopoulos‚ Mark van der Wilk and Ryan D. Greenhalgh

2023.

Details about Current Methods for Drug Property Prediction in the Real World | BibTeX data for Current Methods for Drug Property Prediction in the Real World

Data augmentation in Bayesian neural networks and the cold posterior effect

Seth Nabarro‚ Stoil Ganev‚ Adrià Garriga−Alonso‚ Vincent Fortuin‚ Mark van der Wilk and Laurence Aitchison

In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI). August, 2022.

Details about Data augmentation in Bayesian neural networks and the cold posterior effect | BibTeX data for Data augmentation in Bayesian neural networks and the cold posterior effect | Link to Data augmentation in Bayesian neural networks and the cold posterior effect

Data−Efficient Policy Search using PILCO and Directed−Exploration

Rowan McAllister‚ Mark van der Wilk and Carl Edward Rasmussen

In ICML 2016 Workshop on Data−Efficient Machine Learning. 2016.

Details about Data−Efficient Policy Search using PILCO and Directed−Exploration | BibTeX data for Data−Efficient Policy Search using PILCO and Directed−Exploration | Link to Data−Efficient Policy Search using PILCO and Directed−Exploration

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Vincent Dutordoir‚ James Hensman‚ Mark van der Wilk‚ Carl Henrik Ek‚ Zoubin Ghahramani and Nicolas Durrande

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 34. 2021.

Details about Deep Neural Networks as Point Estimates for Deep Gaussian Processes | BibTeX data for Deep Neural Networks as Point Estimates for Deep Gaussian Processes | Link to Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Design of Experiments for Verifying Biomolecular Networks

Ruby Sedgwick‚ John Goertz‚ Molly Stevens‚ Ruth Misener and Mark van der Wilk

2020.

Details about Design of Experiments for Verifying Biomolecular Networks | BibTeX data for Design of Experiments for Verifying Biomolecular Networks | Link to Design of Experiments for Verifying Biomolecular Networks

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Yarin Gal‚ Mark van der Wilk and Carl Edward Rasmussen

In Z. Ghahramani‚ M. Welling‚ C. Cortes‚ N. D. Lawrence and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27 (NIPS). Pages 3257–3265. Curran Associates‚ Inc.. 2014.

Details about Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models | BibTeX data for Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models | Download (pdf) of Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

GPflux: A Library for Deep Gaussian Processes

Vincent Dutordoir‚ Hugh Salimbeni‚ Eric Hambro‚ John McLeod‚ Felix Leibfried‚ Artem Artemev‚ Mark van der Wilk‚ James Hensman‚ Marc P. Deisenroth and ST John

2021.

Details about GPflux: A Library for Deep Gaussian Processes | BibTeX data for GPflux: A Library for Deep Gaussian Processes | Link to GPflux: A Library for Deep Gaussian Processes

Improved Inverse−Free Variational Bounds for Sparse Gaussian Processes

Mark van der Wilk‚ Artem Artemev and James Hensman

In Fourth Symposium on Advances in Approximate Bayesian Inference. February, 2022.

Details about Improved Inverse−Free Variational Bounds for Sparse Gaussian Processes | BibTeX data for Improved Inverse−Free Variational Bounds for Sparse Gaussian Processes | Link to Improved Inverse−Free Variational Bounds for Sparse Gaussian Processes

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Alexander Immer‚ Tycho F. A. van der Ouderaa‚ Gunnar Rätsch‚ Vincent Fortuin and Mark van der Wilk

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 35. December, 2022.

Details about Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations | BibTeX data for Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations | DOI (10.48550/ARXIV.2202.10638) | Link to Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Last Layer Marginal Likelihood for Invariance Learning

Pola Schwöbel‚ Martin Jørgensen‚ Sebastian W. Ober and Mark van der Wilk

In Proceedings of the Twenty Fifth International Conference on Artificial Intelligence and Statistics (AISTATS). 2022.

Details about Last Layer Marginal Likelihood for Invariance Learning | BibTeX data for Last Layer Marginal Likelihood for Invariance Learning | Link to Last Layer Marginal Likelihood for Invariance Learning

Learning Invariances using the Marginal Likelihood

Mark van der Wilk‚ Matthias Bauer‚ ST John and James Hensman

In S. Bengio‚ H. Wallach‚ H. Larochelle‚ K. Grauman‚ N. Cesa−Bianchi and R. Garnett, editors, Advances in Neural Information Processing Systems 31 (NeurIPS). Pages 9960–9970. Curran Associates‚ Inc.. 2018.

Details about Learning Invariances using the Marginal Likelihood | BibTeX data for Learning Invariances using the Marginal Likelihood | Download (pdf) of Learning Invariances using the Marginal Likelihood

Learning Layer−wise Equivariances Automatically using Gradients

Tycho F. A. van der Ouderaa‚ Alexander Immer and Mark van der Wilk

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 36. December, 2023.

Details about Learning Layer−wise Equivariances Automatically using Gradients | BibTeX data for Learning Layer−wise Equivariances Automatically using Gradients

Learning invariant weights in neural networks

Tycho F.A. van der Ouderaa and Mark van der Wilk

In James Cussens and Kun Zhang, editors, Proceedings of the Thirty−Eighth Conference on Uncertainty in Artificial Intelligence (UAI). Vol. 180 of Proceedings of Machine Learning Research. Pages 1992–2001. PMLR. August, 2022.

Details about Learning invariant weights in neural networks | BibTeX data for Learning invariant weights in neural networks | Link to Learning invariant weights in neural networks

Matrix Inversion free variational inference in Conditional Student's T Processes

Sebastian Popescu‚ Ben Glocker and Mark van der Wilk

In Fourth Symposium on Advances in Approximate Bayesian Inference. February, 2022.

Details about Matrix Inversion free variational inference in Conditional Student's T Processes | BibTeX data for Matrix Inversion free variational inference in Conditional Student's T Processes | Link to Matrix Inversion free variational inference in Conditional Student's T Processes

Memory Safe Computations with XLA Compiler

Artem Artemev‚ Tilman Roeder and Mark van der Wilk

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 35. December, 2022.

Details about Memory Safe Computations with XLA Compiler | BibTeX data for Memory Safe Computations with XLA Compiler | DOI (10.48550/ARXIV.2206.14148) | Link to Memory Safe Computations with XLA Compiler

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

Alexander Terenin‚ David R. Burt‚ Artem Artemev‚ Seth Flaxman‚ Mark van der Wilk‚ Carl Edward Rasmussen and Hong Ge

2022.

Details about Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees | BibTeX data for Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees | DOI (10.48550/ARXIV.2210.07893) | Link to Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

On the Benefits of Invariance in Neural Networks

Clare Lyle‚ Mark van der Wilk‚ Marta Kwiatkowska‚ Yarin Gal and Benjamin Bloem−Reddy

2020.

Details about On the Benefits of Invariance in Neural Networks | BibTeX data for On the Benefits of Invariance in Neural Networks | Link to On the Benefits of Invariance in Neural Networks

Overcoming Mean−Field Approximations in Recurrent Gaussian Process Models

Alessandro Davide Ialongo‚ Mark van der Wilk‚ James Hensman and Carl Edward Rasmussen

In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning (ICML). Vol. 97 of Proceedings of Machine Learning Research. Pages 2931–2940. PMLR. June, 2019.

Details about Overcoming Mean−Field Approximations in Recurrent Gaussian Process Models | BibTeX data for Overcoming Mean−Field Approximations in Recurrent Gaussian Process Models | Link to Overcoming Mean−Field Approximations in Recurrent Gaussian Process Models

Rates of Convergence for Sparse Variational Gaussian Process Regression

David Burt‚ Carl Edward Rasmussen and Mark van der Wilk

In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning (ICML). Vol. 97 of Proceedings of Machine Learning Research. Pages 862–871. PMLR. June, 2019.

Details about Rates of Convergence for Sparse Variational Gaussian Process Regression | BibTeX data for Rates of Convergence for Sparse Variational Gaussian Process Regression | Link to Rates of Convergence for Sparse Variational Gaussian Process Regression

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes

Sebastian W Ober‚ David R Burt‚ Artem Artemev and Mark van der Wilk

In NeurIPS Workshop on Gaussian Processes‚ Spatiotemporal Modeling‚ and Decision−making Systems. December, 2022.

Details about Recommendations for Baselines and Benchmarking Approximate Gaussian Processes | BibTeX data for Recommendations for Baselines and Benchmarking Approximate Gaussian Processes | Download (pdf) of Recommendations for Baselines and Benchmarking Approximate Gaussian Processes

Relaxing Equivariance Constraints with Non−stationary Continuous Filters

Tycho F. A. van der Ouderaa‚ David W. Romero and Mark van der Wilk

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 35. December, 2022.

Details about Relaxing Equivariance Constraints with Non−stationary Continuous Filters | BibTeX data for Relaxing Equivariance Constraints with Non−stationary Continuous Filters | DOI (10.48550/ARXIV.2204.07178) | Link to Relaxing Equivariance Constraints with Non−stationary Continuous Filters

Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes

Creighton Heaukulani and Mark van der Wilk

In H. Wallach‚ H. Larochelle‚ A. Beygelzimer‚ F. Alché−Buc‚ E. Fox and R. Garnett, editors, Advances in Neural Information Processing Systems 32 (NeurIPS). Vol. 32. Curran Associates‚ Inc.. 2019.

Details about Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes | BibTeX data for Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes | Download (pdf) of Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes

SnAKe: Bayesian Optimization with Pathwise Exploration

Jose Pablo Folch‚ Shiqiang Zhang‚ Robert M Lee‚ Behrang Shafei‚ David Walz‚ Calvin Tsay‚ Mark van der Wilk and Ruth Misener

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 35. December, 2022.

Details about SnAKe: Bayesian Optimization with Pathwise Exploration | BibTeX data for SnAKe: Bayesian Optimization with Pathwise Exploration | DOI (10.48550/ARXIV.2202.00060) | Link to SnAKe: Bayesian Optimization with Pathwise Exploration

Sparse Convolutions on Lie Groups

Tycho FA van der Ouderaa and Mark van der Wilk

In NeurIPS Workshop on Symmetry and Geometry in Neural Representations. December, 2022.

Details about Sparse Convolutions on Lie Groups | BibTeX data for Sparse Convolutions on Lie Groups | Link to Sparse Convolutions on Lie Groups

Sparse Gaussian process approximations and applications

Van der Wilk and Mark

PhD Thesis University of Cambridge. 2019.

Details about Sparse Gaussian process approximations and applications | BibTeX data for Sparse Gaussian process approximations and applications | Link to Sparse Gaussian process approximations and applications

Speedy Performance Estimation for Neural Architecture Search

Binxin Ru‚ Clare Lyle‚ Lisa Schut‚ Miroslav Fil‚ Mark van der Wilk and Yarin Gal

In Advances in Neural Information Processing Systems (NeurIPS). Vol. 34. 2021.

Details about Speedy Performance Estimation for Neural Architecture Search | BibTeX data for Speedy Performance Estimation for Neural Architecture Search | Link to Speedy Performance Estimation for Neural Architecture Search

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Alexander Immer‚ Tycho F. A. Van Der Ouderaa‚ Mark Van Der Wilk‚ Gunnar Ratsch and Bernhard Schölkopf

In Proceedings of the 40th International Conference on Machine Learning. 2023.

Details about Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels | BibTeX data for Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels | Link to Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

Miguel Monteiro‚ Loic Le Folgoc‚ Daniel Coelho de Castro‚ Nick Pawlowski‚ Bernardo Marques‚ Konstantinos Kamnitsas‚ Mark van der Wilk and Ben Glocker

In H. Larochelle‚ M. Ranzato‚ R. Hadsell‚ M. F. Balcan and H. Lin, editors, Advances in Neural Information Processing Systems (NeurIPS). Vol. 33. Pages 12756–12767. Curran Associates‚ Inc.. 2020.

Details about Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty | BibTeX data for Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty | Download (pdf) of Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

The promises and pitfalls of deep kernel learning

Sebastian W. Ober‚ Carl E. Rasmussen and Mark van der Wilk

In Cassio de Campos and Marloes H. Maathuis, editors, Proceedings of the Thirty−Seventh Conference on Uncertainty in Artificial Intelligence (UAI). Vol. 161 of Proceedings of Machine Learning Research. Pages 1206–1216. PMLR. July, 2021.

Details about The promises and pitfalls of deep kernel learning | BibTeX data for The promises and pitfalls of deep kernel learning | Link to The promises and pitfalls of deep kernel learning

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

Artem Artemev‚ David R. Burt and Mark van der Wilk

In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning (ICML). Vol. 139 of Proceedings of Machine Learning Research. Pages 362–372. PMLR. July, 2021.

Details about Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients | BibTeX data for Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients | Link to Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

Understanding Probabilistic Sparse Gaussian Process Approximations

Matthias Bauer‚ Mark van der Wilk and Carl Edward Rasmussen

In D. D. Lee‚ M. Sugiyama‚ U. V. Luxburg‚ I. Guyon and R. Garnett, editors, Advances in Neural Information Processing Systems 29 (NIPS). Pages 1533–1541. Curran Associates‚ Inc.. 2016.

Details about Understanding Probabilistic Sparse Gaussian Process Approximations | BibTeX data for Understanding Probabilistic Sparse Gaussian Process Approximations | Download (pdf) of Understanding Probabilistic Sparse Gaussian Process Approximations

Understanding Variational Inference in Function−Space

David R. Burt‚ Sebastian W. Ober‚ Adrià Garriga−Alonso and Mark van der Wilk

In Third Symposium on Advances in Approximate Bayesian Inference. January, 2021.

Details about Understanding Variational Inference in Function−Space | BibTeX data for Understanding Variational Inference in Function−Space | Link to Understanding Variational Inference in Function−Space

Variational Gaussian Process Models without Matrix Inverses

Mark van der Wilk‚ ST John‚ Artem Artemev and James Hensman

In Cheng Zhang‚ Francisco Ruiz‚ Thang Bui‚ Adji Bousso Dieng and Dawen Liang, editors, Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference. Vol. 118 of Proceedings of Machine Learning Research. Pages 1–9. PMLR. January, 2020.

Details about Variational Gaussian Process Models without Matrix Inverses | BibTeX data for Variational Gaussian Process Models without Matrix Inverses | Link to Variational Gaussian Process Models without Matrix Inverses

Variational Inference for Latent Variable Modelling of Correlation Structure

Mark van der Wilk‚ Andrew Gordon Wilson and Carl Edward Rasmussen

In NIPS 2014 Workshop of Advances in Variational Inference. 2014.

Details about Variational Inference for Latent Variable Modelling of Correlation Structure | BibTeX data for Variational Inference for Latent Variable Modelling of Correlation Structure | Link to Variational Inference for Latent Variable Modelling of Correlation Structure

Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models − a Gentle Tutorial

Yarin Gal and Mark van der Wilk

2014.

Details about Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models − a Gentle Tutorial | BibTeX data for Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models − a Gentle Tutorial | Link to Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models − a Gentle Tutorial

Variational Orthogonal Features

David R. Burt‚ Carl Edward Rasmussen and Mark van der Wilk

2020.

Details about Variational Orthogonal Features | BibTeX data for Variational Orthogonal Features | Link to Variational Orthogonal Features

GPflow: A Gaussian Process Library using TensorFlow

Alexander G. de G. Matthews‚ Mark van der Wilk‚ Tom Nickson‚ Keisuke Fujii‚ Alexis Boukouvalas‚ Pablo León−Villagrá‚ Zoubin Ghahramani and James Hensman

In Journal of Machine Learning Research. Vol. 18. No. 40. Pages 1−6. 2017.

Details about GPflow: A Gaussian Process Library using TensorFlow | BibTeX data for GPflow: A Gaussian Process Library using TensorFlow | Link to GPflow: A Gaussian Process Library using TensorFlow