| Time (JST) | Time (Your Browser) | Title | Speaker | Video Link |
18:00-19:00 28/01/2022
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Black-box Adversarial Attacks on Video Recognition Models:
Jingjing Chen (Fudan University) |
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Video |
19:00-20:00 28/01/2022
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A Perspective on Adversarial Robustness:
Sven Gowal (DeepMind) |
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Video |
10:00-11:00 04/02/2022
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A generative approach to robust machine learning:
Vikash Sehwag (Princeton University) |
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Video |
11:00-12:00 04/02/2022
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Role-Based Cooperative Reinforcement Learning:
Tonghan Wang (Tsinghua University & Harvard University) |
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Video |
19:00-20:00 10/02/2022
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Towards Standardized and Accurate Evaluation of the Robustness of Image Classifiers against Adversarial Attacks:
Francesco Croce (University of Tübingen) |
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Video |
20:00-21:00 10/02/2022
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Dataset Condensation for Data-efficient Deep Learning:
Bo Zhao (University of Edinburgh) |
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Video |
10:00-11:00 16/02/2022
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Attacks on Privacy in Federated Learning Scenarios:
Jonas Geiping (University of Maryland) |
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Video |
11:00-12:00 16/02/2022
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Are These Datasets The Same? Learning Kernels for Efficient and Fair Two-sample Tests:
Danica J. Sutherland (University of British Columbia) |
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Video |
12:00-13:00 16/02/2022
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Black-box Adversarial Attacks: From Theory to Practice:
Yinpeng Dong (Tsinghua University) |
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Video |
13:00-14:00 16/02/2022
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Towards Efficient and Effective Adversarial Training:
Sravanti Addepalli (Indian Institute of Science) |
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Video |
10:00-11:00 24/02/2022
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Fair or Robust: Addressing Competing Constraints with Personalized Federated Learning:
Tian Li (Carnegie Mellon University) |
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Video |
11:00-12:00 24/02/2022
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Unveiling Biases in NLP Systems:
Ninareh Mehrabi (University of Southern California) |
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Video |
17:15-18:15 10/03/2022
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Beyond SGD: Efficient Learning with Non i.i.d. Data:
Kfir Y. Levy (Technion) |
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Video |
10:00-11:00 17/03/2022
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Controlling model behavior beyond the training data:
Dimitris Tsipras (Standford University) |
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Video |
10:00-11:00 24/03/2022
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Practical Individual Fairness:
Mikhail Yurochkin (IBM Research) |
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Video |
11:00-12:00 24/03/2022
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Robustness and Accuracy Could be Reconcilable: From Practice to Theory:
Tianyu Pang (Tsinghua University & Sea AI Lab) |
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Video |
10:00-11:00 06/04/2022
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Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks:
Avi Schwarzschild (University of Maryland) |
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Video |
11:00-12:00 06/04/2022
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Adversarial Training for Good:
Chen Zhu (University of Maryland) |
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Video |
10:00-11:00 14/04/2022
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Adversarial purification with score-based generative model:
Jongmin Yoon (KAIST) |
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Video |
11:00-12:00 14/04/2022
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Towards Robust Representation Learning and Beyond:
Cihang Xie (UCSC) |
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Video |
10:00-11:00 22/04/2022
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Private measurement of nonlinear correlations between data hosted across multiple parties:
Praneeth Vepakomma (MIT) |
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Video |
11:00-12:00 22/04/2022
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Advances and Perspectives of Transfer Learning:
Ximei Wang (Tsinghua University) |
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Video |
11:00-12:00 26/04/2022
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Is Sparsity a Good Friend of Robustness?
Zhangyang “Atlas” Wang (University of Texas at Austin & Amazon Research) |
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Video |
10:00-11:00 03/05/2022
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Learning to Generate Data by Estimating Gradients of the Data Distribution:
Yang Song (Stanford University) |
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Video |
11:00-12:00 03/05/2022
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Towards Trustworthy Machine Learning – From Robustness to Fairness:
Boyu Wang (University of Western Ontario) |
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Video |
15:00-16:00 10/05/2022
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Deconstructing Distributions: A Pointwise Framework of Learning:
Gal Kaplun (Harvard University) |
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Video |
17:00-18:00 25/05/2022
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How to learn powerful two-sample tests:
Jonas Kübler (IMPRS-IS) |
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Video |
10:00-11:00 31/05/2022
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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) |
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Video |
11:00-12:00 31/05/2022
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Adversarial attacks towards audio recognition systems:
Yuxuan Chen (Shandong University) |
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Video |
10:00-11:00 02/06/2022
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Debuggable deep networks:
Eric Wong (MIT) |
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Video |
17:00-18:00 20/06/2022
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Noisy Correspondences: A New Paradigm for Learning with Noisy Labels:
Mouxing Yang (Sichuan Univerisity) |
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Video |
16:00-17:00 21/06/2022
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Partial success in closing the gap between human and machine vision:
Robert Geirhos (University of Tübingen) |
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Video |
16:00-17:00 27/06/2022
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Deep Learning Through the Lens of Example Difficulty:
Robert Baldock (Aleph Alpha) |
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No video |
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