Publications

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.

Coulson, M., Gaunt, R. and Reinert, G. (2016) “Poisson approximation of subgraph counts in stochastic block models and a graphon model”, ESAIM: Probability and Statistics [Preprint].

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.

Daly, A. et al. (2015) “Hodgkin–Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods”, Royal Society Open Science, 2(12), p. Article number: 150499.

Stiphout, R. et al. (2015) “Abstract B1-56: Distinct roles of copy number and loss-of-heterozygosity in predicting prognosis for breast cancer patients”, in Cancer Research. American Association for Cancer Research (AACR), pp. b1–56.