Publications

Gray-Davies, T., Holmes, C. and Caron, F. (2016) “Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks”, Electronic Journal of Statistics, 10(2), pp. 1807–1828.

Naik, C. et al. (2016) “Bayesian Nonparametrics for Sparse Dynamic Networks”, arXiv.

Balog, M. et al. (2016) “The Mondrian kernel”, in UAI’16: Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence. AUAI Press, pp. 32–41.

Lomelí, M., Favaro, S. and Teh, Y. (2016) “A marginal sampler for σ-stable Poisson–Kingman mixture models”, Journal of Computational and Graphical Statistics, 26(1), pp. 44–53.

Mitrovic, J., Sejdinovic, D. and Teh, Y.-W. (2016) “DR-ABC: Approximate Bayesian computation with kernel-based distribution regression”, in ICML 2016: 33rd International Conference on Machine Learning. Journal of Machine Learning Research.

Majeed, A. et al. (2016) “How would a decision to leave the European Union affect medical research and health in the United Kingdom?”, Journal of the Royal Society of Medicine, 109(6), pp. 216–218.

Sodjo, J. et al. (2016) “Joint Segmentation of Multiple Images with Shared Classes: A Bayesian Nonparametrics Approach”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 1–5.

Kim, H. et al. (2016) “Collaborative Filtering with Side Information: a Gaussian Process Perspective.”

Lakshminarayanan, B., Roy, D. and Teh, Y. (2016) “Mondrian Forests for Large-Scale Regression when Uncertainty Matters”, in Proceedings of Machine Learning Research. 19th International Conference on Artificial Intelligence and Statistics, pp. 1478–1487.