I am Yaowen Ye (or Elwin, or 叶耀文), a senior majoring in Computer Science at the University of Hong Kong. I am interested in making human oversight of AI systems reliable on hard tasks and efficient for complex outputs. Currently, I am working in Prof. Jacob Steinhardt's group. Before this, I was fortunate to be advised by Prof. Yixin Zhu at PKU Cognitive Reasoning Lab and Prof. Chao Huang at HKU Data Intelligence Lab.
Links:
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Research Interests
I am interested in making human oversight of AI systems reliable on hard tasks and efficient for complex outputs. Some problems I am thinking about recently are:
- Alignment under unreliable supervision. As humans inevitably make mistakes due to limited capabilities or cognitive and social biases, we need methods that ensure reliable alignment even with systematically flawed supervision.
- Scaling human oversight to interacting AI systems. AI systems will be deployed at massive scale due to their growing capabilities and cost-effectiveness. We need algorithms and tools that help humans efficiently understand the complex outputs of multiple interacting AI systems.
- Scaling laws for human oversight. We need better evaluation of oversight methods, instead of merely measuring accuracy. I aim to develop empirically-grounded scaling laws that help us predict human resource requirements for reliably supervising an AI system on a specific task, hence revealing which oversight methods may remain practical in the future.
In the past, I also worked on
- Cognitive reasoning: How can we explain humans’ reasoning process such as intuitive physics and abductive reasoning? How do these explanations inspire understanding of AI?
- Learning on graphs: How can we better model graph data for applications like recommender systems? How can we ensure these models are designed equally for different users and responsibly for real-world deployment?
Publications
2024
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision.
Under review.
Yaowen Ye*, Cassidy Laidlaw* and Jacob Steinhardt. [pdf]
2023
Graph Masked Autoencoder for Sequential Recommendation.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'23)
Yaowen Ye, Chao Huang and Lianghao Xia. [pdf]
Masked Graph Transformer for Recommendation.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'23)
Chaoliu Li, Chao Huang, Lianghao Xia, Xubin Ren, Yaowen Ye and Yong Xu. [pdf]