Event Time: 

Thursday, 5 November @ 1700 (AEDT) (Melbourne)
Thursday, 5 November @ 1400 (CST) (Beijing, China)
Thursday, 5 November @ 1130 (IST) (New Delhi, India)
Thursday, 5 November @ 0600 (GMT) (London)
Thursday, 5 November @ 0700 (CET) (Zürich, Switzerland)
Thursday, 5 November @ 0100 (EST) (New York)
Wednesday, 4 November @ 2200 (PST) (Seattle)

Presenter: Professor Peter Bühlmann, ETH Zürich

Biography:  ETH Zürich & Wikipedia

Title: Invariance, Causality and Robustness in Statistical Learning

Abstract: Interpretable and reliable machine learning and artificial intelligence is a big emerging theme, complementing the development of pure black box prediction tools. Looking through the lens of statistical causality and exploiting a probabilistic invariance property opens up new paths and opportunities for enhanced robustness, with wide-ranging prospects for various applications.

Structure: 45 minutes seminar with 15 minutes question time

Seminar Recording & Slides:

Please click here for the recording of the webinar.

Please click here for the presentation slides.