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

Time (JST)Time (Your Browser)TitleSpeaker 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)

Registration