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Contact and Information:

Korbinian Strimmer and Thomas House
University of Manchester
Academic Year 2017-18

Begin: 23 January 2017
End: June 2018
Time: 2pm-3pm
Place: Horace Lamb Meeting Room, 1st Floor, Alan Turing Building


In this journal club we aim to discuss in an informal way methodological aspects in statistics, machine learning, and data science by reading current relevant papers. We meet in the Alan Turing building (School of Mathematics). All interested statisticians and computational scientists in Manchester welcome!


We start the sessions on 23 Januar 2018 and then meet regulary about twice a month. For precise dates and links to all the papers discussed see below!

Announcements concerning this journal club are also sent via the MATH-STATSML-CLUB mailing list. Please join this list if you would like to receive regular updates by email.


Session Date Topic
7 Tuesday 12 June 2018 Vehtari et al. 2018. Expectation propagation as a way of life: a framework for Bayesian inference on partitioned data. arXiv:1412.4869
Background reading:
Raymond et al. 2014. Expectation propagation. arXiv:1409.6179; Seeger. 2008. Expectation propagation for exponential families; Minka. 2001. Expectation propagation for approximate Bayesian inference. Proc. Conf. UAI 17:362-369; Blei et al. 2017. Variational inference: a review for statisticians. JASA 112: 859-877.
6 Thursday 31 May 2018 Wong et al. 2016. A frequentist approach to computer model calibration. JRSS B 79: 635-648;
Plumlee. 2017. Bayesian calibration of inexact computer models. JASA 112:1274-1285.
5 Thursday 26 April 2018 Svensson, Dahlin, and Schön. 2015. Marginalizing Gaussian process hyperparameters using sequential Monte Carlo. IEEE Proceedings CAMSAP 6: 477-480.
Background reading:
Del Moral, Doucet, and Jasra. 2006. Sequential Monte Carlo samplers. JRSS B 68: 411-436;
Rasmussen and Williams. 2006. Gaussian processes for machine learning. MIT Press.
4 Monday 19 March 2018 Stephens. 2017. False discovery rates: a new deal. Biostatistics 18:275-294
3 Monday 19 February 2018 Rainforth and Wood. 2017. Canonical correlation forests. arXiv:1507.05444;
Fernández-Delgado et al. 2014. Do we need hundreds of classifiers to solve real world classification problems? JMLR 15:3133-3181
2 Monday 5 February 2018 Wang et al 2016. Bayesian optimization in a billion dimensions via random embeddings. JAIR 55: 361-387
1 Tuesday 23 January 2018 Vidal et al. 2017. Mathematics of deep learning. arXiv:1712.04741;
Marcus. 2018. Deep learning: a critical appraisal. arXiv:1801.00631
2015-2017 Papers discussed in an earlier journal club run at Imperial College London.