5 Nov 2020
5:00 pm - 6:00 pm
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.