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.

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.

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.

Titsias, M., Holmes, C. and Yau, C. (2016) “Statistical Inference in Hidden Markov Models Using k-Segment Constraints”, Journal of the American Statistical Association, 111(513), pp. 200–215.

Teh, Y., Thiery, A. and Vollmer, S. (2016) “Consistency and fluctuations for stochastic gradient Langevin dynamics”, Journal of Machine Learning Research, 17(7), pp. 1–33.

Rebeschini, P. and Tatikonda, S. (2016) “Decay of Correlation in Network Flow Problems”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 169–174.