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(2020). Translation Insensitivity for Deep Convolutional Gaussian Processes. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020).

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(2019). Variational Gaussian Process Models without Matrix Inverses. 2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019).

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

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

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

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

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(2018). Non-Factorised Variational Inference in Dynamical Systems. Symposium on Advances in Approximate Bayesian Inference.

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

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

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

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

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(2016). GPflow: A Gaussian Process Library using TensorFlow. Journal of Machine Learning Research.

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

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