| Time (JST) | Time (Your Browser) | Title | Speaker | Video Link |
10:00-11:00 05/07/2022
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Toward a Principled Understanding of Robust Machine Learning Methods and Its Connection to Multiple Aspects:
Haohan Wang (Carnegie Mellon University) |
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Video |
9:00-10:00 08/07/2022
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Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization:
Junyuan Hong (Michigan State University) |
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Video |
11:00-12:00 20/07/2022
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Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence:
Biwei Huang (Carnegie Mellon University) |
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Video |
15:30-16:30 26/07/2022
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Deep Learning for Biosequences:
Jean-Philippe Vert (Google Research) |
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Video |
10:00-11:00 08/08/2022
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The Matthew Effect when Learning from Weakly Supervised Data:
Yang Liu (UCSC) |
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Video |
9:00-10:00 16/08/2022
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A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models:
Chhavi Yadav (UCSD) |
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Video |
10:00-11:00 16/08/2022
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Understanding Pre-Training, Fine-Tuning, and Self-Training for Unsupervised Domain Adaptation:
Ananya Kumar (Stanford University) |
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Video |
10:00-11:00 24/08/2022
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Enabling Large-Scale Certifiable Deep Learning towards Trustworthy Machine Learning:
Linyi Li (UIUC) |
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Video |
11:00-12:00 01/09/2022
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Predicting Out-of-Distribution Error with the Projection Norm:
Yaodong Yu (University of California, Berkeley) |
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Video |
10:00-11:00 02/09/2022
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Understanding Dataset Difficulty with V-Usable Information:
Kawin Ethayarajh (Stanford University) |
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Video |
15:00-16:00 02/09/2022
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On the Impact of Estimating Example Difficulty:
Chirag Agarwal (Adobe) |
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Video |
9:00-10:00 06/09/2022
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Understanding Probability Estimation and Noisy Label Learning: From the Early Learning Perspective:
Sheng Liu (New York University) |
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Video |
9:00-10:00 16/09/2022
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Provably calibrating ML classifiers without distributional assumptions:
Chirag Gupta (Carnegie Mellon University) |
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Video |
10:00-11:00 16/09/2022
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Humanly Certify Superhuman Classifiers:
Qiongkai Xu (University of Melbourne) |
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No video |
14:30-15:30 16/09/2022
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Recent Advances in Domain Adaptation and Generalization:
Mahsa Baktashmotlagh (University of Queensland) |
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Video |
10:00-11:00 06/10/2022
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Challenges and Opportunities in Out-of-distribution Detection:
Sharon Y. Li (University of Wisconsin Madison) |
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Video |
10:00-11:00 19/10/2022
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Online Adaptation to Label Distribution Shift:
Ruihan Wu (Cornell University) |
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Video |
15:00-16:00 26/10/2022
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A Framework of Weakly Supervised Learning by Semi-Supervised Learning:
Zhuowei Wang (Commonwealth Scientific and Industrial Research Organization, Australia) |
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Video |
16:00-17:00 26/10/2022
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Statistical Aspects of Trustworthy Machine Learning
Nikola Konstantinov (ETH AI Center) |
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Video |
16:00-17:00 28/10/2022
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Explainable Artificial Intelligence: Academic Research and Industrial Applications in Korea
Jaesik Choi (KAIST) |
![../../pic/Jaesik_choi.png]()
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Video |
11:00-12:00 31/10/2022
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Toward Efficient Evaluation and Training of Adversarially Robust Neural Networks:
Gaurang Sriramanan (University of Maryland) |
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Video |
14:00-15:00 31/10/2022
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A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search:
Tuan Dam (TU Darmstadt) |
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Video |
14:00-15:00 1/11/2022
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Monte Carlo simulation of physical systems with deep generative models:
Shinichi Nakajima (Technische Universität Berlin) |
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Video |
16:30-17:30 16/11/2022
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Trustworthy AI in SmartHealth and a case-study in Vietnam:
Phi Le Nguyen (Hanoi University of Science and Technology) |
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Video |
15:00-16:00 25/11/2022
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Towards Adversarial Robustness of Deep Vision Algorithms:
Hanshu Yan (ByteDance) |
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Video |
16:00-17:00 7/12/2022
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Model Adaptation under Domain and Category Shift:
Shiqi Yang (Autonomous University of Barcelona) |
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Video |
17:00-18:00 7/12/2022
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3D Common Corruptions and Data Augmentation:
Oğuzhan Fatih Kar (EPFL) |
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Video |
19:00-20:00 9/12/2022
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Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs:
Ezgi Korkma (DeepMind) |
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No video |
17:00-18:00 13/12/2022
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Robustness via Cross Domain Ensembles:
Teresa Yeo (EPFL) |
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Video |
15:30-16:30 21/12/2022
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On the Predictive Power of Graph Neural Networks:
Weihua Hu (Stanford University) |
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Video |
10:00-11:00 29/12/2022
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Trustworthy Machine Learning via Learning with Reasoning:
Bo Li (UIUC) |
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Video |
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