I am Yaowen Ye (or Elwin, or 叶耀文), a senior majoring in Computer Science at the University of Hong Kong and an incoming PhD student at UC Berkeley. 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:
[X] [Email] [Give me feedback!]
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, intuitive physics, learning on graphs, and recommender systems.
Publications
2025
Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision.
International Conference on Learning Representations (ICLR), 2025. Spotlight presentation.
Yaowen Ye*, Cassidy Laidlaw* and Jacob Steinhardt.
[paper]
2023
Graph Masked Autoencoder for Sequential Recommendation.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023.
Yaowen Ye, Chao Huang and Lianghao Xia.
[paper]
Masked Graph Transformer for Recommendation.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023.
Chaoliu Li, Chao Huang, Lianghao Xia, Xubin Ren, Yaowen Ye and Yong Xu.
[paper]