Timetable for the TrustML YSS online seminars from May to Jun., 2022.

Time (JST)	Time (Your Browser)	Title	Speaker	Video Link
10:00-11:00 03/05/2022		Learning to Generate Data by Estimating Gradients of the Data Distribution: Yang Song (Stanford University)		Video
11:00-12:00 03/05/2022		Towards Trustworthy Machine Learning – From Robustness to Fairness: Boyu Wang (University of Western Ontario)		Video
15:00-16:00 10/05/2022		Deconstructing Distributions: A Pointwise Framework of Learning: Gal Kaplun (Harvard University)		Video
17:00-18:00 25/05/2022		How to learn powerful two-sample tests: Jonas Kübler (IMPRS-IS)		Video
10:00-11:00 31/05/2022		Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary: Takashi Ishida (The University of Tokyo)		Video
11:00-12:00 31/05/2022		Adversarial attacks towards audio recognition systems: Yuxuan Chen (Shandong University)		Video
10:00-11:00 02/06/2022		Debuggable deep networks: Eric Wong (MIT)		Video
17:00-18:00 20/06/2022		Noisy Correspondences: A New Paradigm for Learning with Noisy Labels: Mouxing Yang (Sichuan Univerisity)		Video
16:00-17:00 21/06/2022		Partial success in closing the gap between human and machine vision: Robert Geirhos (University of Tübingen)		Video
16:00-17:00 27/06/2022		Deep Learning Through the Lens of Example Difficulty: Robert Baldock (Aleph Alpha)		No video