Yuzhe Ma
Papers
1
Total Citations
3
H-Index
1
About
Yuzhe Ma is a rising researcher whose work lies at the intersection of reinforcement learning, algorithmic teaching, and machine learning theory. His most notable contribution is a rigorous analysis of the sample complexity involved in teaching agents through reinforcement—a paradigm he distinguishes from traditional teaching-by-demonstration. In his highly cited 2021 paper, Ma formally defines and characterizes the "teaching dimension" for Q-learning, showing how a teacher can strategically shape a student’s learning process through carefully designed rewards. This work provides foundational theoretical insights into how efficiently an agent can be guided to an optimal policy, with implications for AI safety, personalized tutoring systems, and human-AI interaction. While his citation count is still growing, Ma’s research is recognized for its clarity and depth, earning him attention from both the reinforcement learning and educational data mining communities. His work is particularly valuable for students and researchers interested in understanding the fundamental limits and possibilities of teaching machines, bridging theoretical computer science with practical learning paradigms.
Research Focus
Key Achievements
Top Papers
- 1The Sample Complexity of Teaching by Reinforcement on Q-Learning3 citations · 2021