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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 link to follow

Upcoming talks

1 Oct 2020: P. Richard Hahn (Arizona State University) 16:00-17:00

https://math.la.asu.edu/~prhahn/

(Title and abstract TBC)

8 Oct 2020: Sandipan Roy (University of Bath)

https://researchportal.bath.ac.uk/en/persons/sandipan-roy

(Title and abstract TBC)

15 Oct 2020: Fan Li (Duke University)

https://www2.stat.duke.edu/~fl35/

(Title and abstract TBC)

22 Oct 2020: Aude Genevay (Massachusetts Institute of Technology)

https://audeg.github.io/

(Title and abstract TBC)

29 Oct 2020: Rainer Winkelmann (University of Zurich)

https://www.econ.uzh.ch/en/people/faculty/winkelmann.html

(Title and abstract TBC)

05 Nov 2020: Maryclare Griffin (University of Massachusetts Amherst)

https://maryclare.github.io

(Title and abstract TBC)

12 Nov 2020: Debasish Roy (Indian Institute of Science)

http://civil.iisc.ernet.in/~royd/

(Title and abstract TBC)

19 Nov 2020: Tra My Pham (UCL)

https://iris.ucl.ac.uk/iris/browse/profile?upi=TMPHA59

(Title and abstract TBC)

26 Nov 2020: Alejandra Avalos Pacheco (Harvard)

https://sites.google.com/view/aleavalos

(Title and abstract TBC)

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