Publications

(2025). Turbulence: Systematically and automatically testing instruction-tuned large language models for code. 2025 IEEE Conference on Software Testing, Verification and Validation (ICST).

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(2025). Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays. Biotechnology and Bioengineering.

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(2025). SynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference. ICML 2025 Workshop on Methods and Opportunities at Small Scale.

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(2025). Rethinking Aleatoric and Epistemic Uncertainty. Forty-second International Conference on Machine Learning (ICML).

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(2025). Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning. ICML 2025 Workshop on Scaling Up Intervention Models.

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(2025). Continuous Bayesian Model Selection for Multivariate Causal Discovery. Forty-second International Conference on Machine Learning (ICML).

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(2025). Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?. Forty-second International Conference on Machine Learning (ICML).

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(2025). A Meta-Learning Approach to Bayesian Causal Discovery. The Thirteenth International Conference on Learning Representations (ICLR).

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(2024). Time-varying functional connectivity as Wishart processes. Imaging Neuroscience.

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(2024). Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks. ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling.

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(2024). Transition Constrained Bayesian Optimization via Markov Decision Processes. Advances in Neural Information Processing Systems 37 (NeurIPS).

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(2024). Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. Journal of Machine Learning Research.

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(2024). Noether' s Razor: Learning Conserved Quantities. Advances in Neural Information Processing Systems 37 (NeurIPS).

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(2024). Learning in Deep Factor Graphs with Gaussian Belief Propagation. Forty-first International Conference on Machine Learning (ICML).

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(2024). Inverse-Free Sparse Variational Gaussian Processes. NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty.

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(2024). Bivariate Causal Discovery using Bayesian Model Selection. Forty-first International Conference on Machine Learning (ICML).

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(2023). Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. Proceedings of the 40th International Conference on Machine Learning (ICML).

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(2023). Practical Path-based Bayesian Optimization. NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World.

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(2023). Learning Layer-wise Equivariances Automatically using Gradients. Advances in Neural Information Processing Systems (NeurIPS).

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(2023). Current Methods for Drug Property Prediction in the Real World.

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(2023). Competitive Amplification Networks enable molecular pattern recognition with PCR. bioRxiv.

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(2023). Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization. Computers & Chemical Engineering.

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(2023). Actually Sparse Variational Gaussian Processes. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2022). Sparse Convolutions on Lie Groups. NeurIPS Workshop on Symmetry and Geometry in Neural Representations.

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(2022). SnAKe: Bayesian Optimization with Pathwise Exploration. Advances in Neural Information Processing Systems (NeurIPS).

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(2022). Relaxing Equivariance Constraints with Non-stationary Continuous Filters. Advances in Neural Information Processing Systems (NeurIPS).

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(2022). Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems.

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(2022). Memory Safe Computations with XLA Compiler. Advances in Neural Information Processing Systems (NeurIPS).

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(2022). Matrix Inversion free variational inference in Conditional Student's T Processes. Fourth Symposium on Advances in Approximate Bayesian Inference.

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(2022). Learning invariant weights in neural networks. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI).

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(2022). Last Layer Marginal Likelihood for Invariance Learning. Proceedings of the Twenty Fifth International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2022). Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. Advances in Neural Information Processing Systems (NeurIPS).

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(2022). Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes. Fourth Symposium on Advances in Approximate Bayesian Inference.

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(2022). Data augmentation in Bayesian neural networks and the cold posterior effect. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI).

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(2022). Causal Discovery using Marginal Likelihood. NeurIPS Workshop on Causality for Real-world Impact.

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(2022). Bayesian Neural Network Priors Revisited. International Conference on Learning Representations (ICLR).

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

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(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 Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI).

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

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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). Correlated weights in infinite limits of deep convolutional neural networks. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI).

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

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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. NeurIPS 2020 Workshop on Machine Learning for Molecules.

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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). 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). 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). 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). 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). 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. International Joint Conferences on Artificial Intelligence (IJCAI).

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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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