Publications

(2022). Optimizing Sequential Experimental Design with Deep Reinforcement Learning. International Conference on Machine Learning (ICML).

(2022). Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks. International Conference on Machine Learning (ICML).

(2021). Model Selection for Bayesian Autoencoders. Advances in Neural Information Processing Systems (NeurIPS).

(2021). BORE: Bayesian Optimization by Density-Ratio Estimation. International Conference on Machine Learning (ICML).

(2021). Sparse Gaussian processes revisited: Bayesian approaches to inducing-variable approximations. International Conference on Artificial Intelligence and Statistics (AISTATS).

(2021). SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. International Conference on Machine Learning (ICML).

(2021). Distribution Regression for Sequential Data. International Conference on Artificial Intelligence and Statistics (AISTATS).

(2020). Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. Advances in Neural Information Processing Systems (NeurIPS).

(2020). Quantile Propagation for Wasserstein-Approximate Gaussian Processes. Advances in Neural Information Processing Systems (NeurIPS).

(2020). Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. arXiv preprint arXiv:2003.03080.

(2020). Distribution Regression for Continuous-Time Processes via the Expected Signature. arXiv preprint arXiv:2006.05805.

(2019). Variational Graph Convolutional Networks. NeurIPS Workshop on Graph Representational Learning.

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(2019). Structured Variational Inference in Continuous Cox Process Models. Advances in Neural Information Processing Systems (NeurIPS).

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(2019). Sparse Grouped Gaussian Processes for Solar Power Forecasting.. CoRR.

(2019). Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations. arXiv preprint arXiv:1903.03986.

(2019). Grouped Gaussian processes for solar power prediction. Machine Learning.

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(2019). Generic Inference in Latent Gaussian Process Models.. Journal of Machine Learning Research.

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(2019). Efficient Inference in Multi-task Cox Process Models. International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2019). Calibrating Deep Convolutional Gaussian Processes. International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2018). Variational Network Inference: Strong and Stable with Concrete Support. International Conference on Machine Learning (ICML).

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(2018). Cycle-consistent adversarial learning as approximate bayesian inference. arXiv preprint arXiv:1806.01771.

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(2017). Scalable Gaussian process models for solar power forecasting. International Workshop on Data Analytics for Renewable Energy Integration.

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(2017). Random feature expansions for deep Gaussian processes. International Conference on Machine Learning (ICML).

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(2017). Gray-box inference for structured Gaussian process models. International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2017). AutoGP: Exploring the capabilities and limitations of Gaussian process models. Uncertainty in Artificial Intelligence.

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(2016). Extended and unscented kitchen sinks. International Conference on Machine Learning (ICML).

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(2016). Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels. NIPS Workshop on Bayesian Deep Learning.

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(2015). Scalable inference for Gaussian process models with black-box likelihoods. Advances in Neural Information Processing Systems (NeurIPS).

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(2014). Inference Engine Final Report to ARENA Inference Engine Final Report to ARENA.

(2014). Fast allocation of Gaussian process experts. International Conference on Machine Learning (ICML).

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(2014). Extended and unscented Gaussian processes. Advances in Neural Information Processing Systems (NeurIPS).

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(2014). Distributed Bayesian geophysical inversions. Thirty-Ninth Stanford Geothermal Workshop.

(2014). Collaborative Multi-output Gaussian Processes.. Uncertainty in Artificial Intelligence.

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(2014). Automatic feature generation for machine learning--based optimising compilation. ACM Transactions on Architecture and Code Optimization.

(2014). Automated variational inference for Gaussian process models. Advances in Neural Information Processing Systems (NeurIPS).

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(2013). Learning community-based preferences via dirichlet process mixtures of gaussian processes. International joint conference on artificial intelligence.

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(2013). Efficient Variational Inference for Gaussian Process Regression Networks. International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2013). Dynamic microarchitectural adaptation using machine learning. ACM Transactions on Architecture and Code Optimization (TACO).

(2013). Decision-theoretic sparsification for Gaussian process preference learning. Joint European Conference on Machine Learning and Knowledge Discovery in Databases.

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(2013). Bayesian joint inversions for the exploration of earth resources. Twenty-Third International Joint Conference on Artificial Intelligence.

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(2012). Predicting best design trade-offs: A case study in processor customization. 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE).

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(2012). New objective functions for social collaborative filtering. Proceedings of the 21st international conference on World Wide Web.

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(2012). Discriminative probabilistic prototype learning. International Conference on Machine Learning (ICML).

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(2012). Bayesian data fusion for geothermal exploration. The Australian Geothermal Energy Association (AGEA).

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(2011). Sparse gaussian processes for learning preferences. Proceedings of NIPS Workshop on Choice Models and Preference Learning (CMPL).

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(2011). Milepost gcc: Machine learning enabled self-tuning compiler. International journal of parallel programming.

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(2011). Improving Topic Coherence with Regularized Topic Models. Advances in Neural Information Processing Systems (NeurIPS).

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(2010). Gaussian process preference elicitation. Advances in Neural Information Processing Systems (NeurIPS).

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(2010). A predictive model for dynamic microarchitectural adaptivity control. 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture.

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(2009). Portable compiler optimisation across embedded programs and microarchitectures using machine learning. Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture.

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(2009). Automatic feature generation for machine learning based optimizing compilation. International Symposium on Code Generation and Optimization.

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(2008). Multi-task Gaussian process prediction. Advances in Neural information processing systems (NeurIPS).

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(2008). MILEPOST GCC: machine learning based research compiler. GCC Summit.

(2008). Compilers that learn to optimise: a probabilistic machine learning approach.

(2007). Rapidly selecting good compiler optimizations using performance counters. International Symposium on Code Generation and Optimization.

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(2007). Kernel multi-task learning using task-specific features. Artificial Intelligence and Statistics (AISTATS).

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(2007). A note on noise-free Gaussian process prediction with separable covariance functions and grid designs.

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(2006). Using machine learning to focus iterative optimization. International Symposium on Code Generation and Optimization.

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(2006). Predictive search distributions. International Conference on Machine Learning (ICML).

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(2006). Automatic performance model construction for the fast software exploration of new hardware designs. International conference on Compilers, architecture and synthesis for embedded systems.

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(2004). Predicting Good Compiler Transformations Using Machine Learning.