Publications

(2021). Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. Proceedings of the 38th International Conference on Machine Learning (ICML).

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(2021). The Promises and Pitfalls of Deep Kernel Learning. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI).

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(2021). Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI).

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(2021). Understanding Variational Inference in Function-Space. Third Symposium on Advances in Approximate Bayesian Inference.

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(2021). Speedy Performance Estimation for Neural Architecture Search. Advances in Neural Information Processing Systems (NeurIPS).

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(2021). Learning Invariant Weights in Neural Networks. ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning.

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(2021). Last Layer Marginal Likelihood for Invariance Learning.

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(2021). GPflux: A Library for Deep Gaussian Processes.

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(2021). Deep Neural Networks as Point Estimates for Deep Gaussian Processes. Advances in Neural Information Processing Systems (NeurIPS).

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(2021). Data augmentation in Bayesian neural networks and the cold posterior effect.

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(2021). Bayesian Neural Network Priors Revisited.

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(2021). Barely Biased Learning for Gaussian Process Regression.

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(2020). Bayesian Image Classification with Deep Convolutional Gaussian Processes. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2020). Variational Orthogonal Features.

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(2020). Variational Gaussian Process Models without Matrix Inverses. Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference.

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(2020). Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. Advances in Neural Information Processing Systems (NeurIPS).

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(2020). On the Benefits of Invariance in Neural Networks.

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(2020). Design of Experiments for Verifying Biomolecular Networks.

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(2020). Convergence of Sparse Variational Inference in Gaussian Processes Regression. Journal of Machine Learning Research.

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(2020). Capsule Networks -- A Probabilistic Perspective.

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(2020). A Framework for Interdomain and Multioutput Gaussian Processes.

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(2020). A Bayesian Perspective on Training Speed and Model Selection. Advances in Neural Information Processing Systems (NeurIPS).

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(2019). Rates of Convergence for Sparse Variational Gaussian Process Regression. Proceedings of the 36th International Conference on Machine Learning (ICML).

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(2019). Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. Proceedings of the 36th International Conference on Machine Learning (ICML).

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(2019). Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. Advances in Neural Information Processing Systems 32 (NeurIPS).

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(2019). Bayesian Layers: A Module for Neural Network Uncertainty. Advances in Neural Information Processing Systems 32 (NeurIPS).

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(2018). Learning Invariances using the Marginal Likelihood. Advances in Neural Information Processing Systems 31 (NeurIPS).

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(2017). GPflow: A Gaussian Process Library using TensorFlow. Journal of Machine Learning Research.

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(2017). Convolutional Gaussian Processes. Advances in Neural Information Processing Systems 30 (NIPS).

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(2017). Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning.

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(2017). Closed-form Inference and Prediction in Gaussian Process State-Space Models. NIPS 2017 Workshop on Time Series.

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(2016). Understanding Probabilistic Sparse Gaussian Process Approximations. Advances in Neural Information Processing Systems 29 (NIPS).

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(2016). Data-Efficient Policy Search using PILCO and Directed-Exploration. ICML 2016 Workshop on Data-Efficient Machine Learning.

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(2014). Variational Inference for Latent Variable Modelling of Correlation Structure. NIPS 2014 Workshop of Advances in Variational Inference.

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(2014). Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. Advances in Neural Information Processing Systems 27 (NIPS).

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