Timetable for the TrustML YSS online seminars from Jan. to Feb., 2022.

Time (JST)	Time (Your Browser)	Title	Speaker	Video Link
18:00-19:00 28/01/2022		Black-box Adversarial Attacks on Video Recognition Models: Jingjing Chen (Fudan University)		Video
19:00-20:00 28/01/2022		A Perspective on Adversarial Robustness: Sven Gowal (DeepMind)		Video
10:00-11:00 04/02/2022		A generative approach to robust machine learning: Vikash Sehwag (Princeton University)		Video
11:00-12:00 04/02/2022		Role-Based Cooperative Reinforcement Learning: Tonghan Wang (Tsinghua University & Harvard University)		Video
19:00-20:00 10/02/2022		Towards Standardized and Accurate Evaluation of the Robustness of Image Classifiers against Adversarial Attacks: Francesco Croce (University of Tübingen)		Video
20:00-21:00 10/02/2022		Dataset Condensation for Data-efficient Deep Learning: Bo Zhao (University of Edinburgh)		Video
10:00-11:00 16/02/2022		Attacks on Privacy in Federated Learning Scenarios: Jonas Geiping (University of Maryland)		Video
11:00-12:00 16/02/2022		Are These Datasets The Same? Learning Kernels for Efficient and Fair Two-sample Tests: Danica J. Sutherland (University of British Columbia)		Video
12:00-13:00 16/02/2022		Black-box Adversarial Attacks: From Theory to Practice: Yinpeng Dong (Tsinghua University)		Video
13:00-14:00 16/02/2022		Towards Efficient and Effective Adversarial Training: Sravanti Addepalli (Indian Institute of Science)		Video
10:00-11:00 24/02/2022		Fair or Robust: Addressing Competing Constraints with Personalized Federated Learning: Tian Li (Carnegie Mellon University)		Video
11:00-12:00 24/02/2022		Unveiling Biases in NLP Systems: Ninareh Mehrabi (University of Southern California)		Video