Publications

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.

Todeschini, A., Miscouridou, X. and Caron, F. (2016) “Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities”, arXiv.

Teh, Y. (2016) “Bayesian Nonparametric Modeling and the Ubiquitous Ewens Sampling Formula”, Statistical Science, 31(1), pp. 34–36.

Vollmer, S., Zygalakis, K. and Teh, Y. (2016) “Exploration of the (non-)asymptotic bias and variance of stochastic gradient Langevin dynamics”, Journal of Machine Learning Research, 17(9), pp. 1–33.

Caron, F. (2016) “Book Review: Nonlinear Time Series: Theory, methods, and applications with R examples, by Randal Douc, Eric Moulines, David S. Stoffer”, Statistics and Computing, 26(1-2), pp. 571–572.

McGree, J. et al. (2016) “Big data and design of experiments”, in Computational and Statistical Methods for Analysing Big Data with Applications, pp. 111–129.