Timetable for the TrustML YSS online seminars from Jan. to Feb., 2022. Time (JST)Time (Your Browser)TitleSpeaker 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
