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Statistical Science

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Statistical Science Seminars

A seminar series covering a broad range of applied and methodological topics in Statistical Science.

*** All talks will take place online until further notice ***

Usual time: Thursdays 14:00-15:00

Location: Zoom

Please email thomas dot bartlett dot 10 at ucl dot ac dot uk to join the mailing list, and receive the links to the talks.

Recent talks

Please subscribe to our Youtube channel, to view some recent talks from the series:

https://youtube.com/channel/UC6wQjF2n27k6a_TRO4GjPEg

Upcoming talks

 

26 Nov 2020: Alejandra Avalos Pacheco (Harvard) - Factor regression for dimensionality reduction and data integration techniques with applications to cancer data

Two key challenges in modern statistical applications are the large amount of information recorded per individual, and that such data are often not collected all at once but in batches. These batch effects can be complex, causing distortions in both mean and variance. We propose a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction while correcting for a range of batch effects. We study the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this paper. We present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality, as well as the need to account for batch effects to obtain reliable results. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity and is highly computationally efficient.
 

03 Dec 2020 Christopher Yau (Manchester / Turing)

https://www.research.manchester.ac.uk/portal/christopher.yau.html

(Title and abstract TBC)

10 Dec 2020: Kristian Lum (University of Pennsylvania)

https://ldi.upenn.edu/expert/kristian-lum-phd-msc

(Title and abstract TBC)

17 Dec 2020: Fulvia Pennoni (University of Milano-Bicocca)

https://sites.google.com/view/fulviapennoni/home

(Title and abstract TBC)

14 Jan 2021: Chris Holmes (Oxford / Turing)

http://www.stats.ox.ac.uk/~cholmes/

(Title and abstract TBC)

21 Jan 2021: Benjamin Guedj (UCL) - A primer on PAC-Bayesian learning, and some application

Abstract: PAC-Bayes is a generic and flexible framework to address generalisation abilities of machine learning algorithms. It leverages the power of Bayesian inference and allows to derive new learning strategies. I will briefly present the key concepts of PAC-Bayes and illustrate a few of its recent successes (including generalisation guarantees for deep neural networks).

References: https://bguedj.github.io/publications/#, and our ICML 2019 tutorial https://bguedj.github.io/icml2019/index.html

28 Jan 2021: Rachael Meager (London School of Economics)

https://www.lse.ac.uk/economics/people/faculty/rachael-meager

(Title and abstract TBC)

Affiliated Seminars