Weihao Kong
Papers
1
Total Citations
6
H-Index
1
About
Weihao Kong is a researcher whose work lies at the intersection of robust statistics, meta-learning, and high-dimensional inference. His research addresses fundamental challenges in modern machine learning, particularly how to learn effectively when data is scarce, noisy, or drawn from heterogeneous sources. Kong’s most cited work, “Robust Meta-learning for Mixed Linear Regression with Small Batches” (2020), tackles a critical practical problem: in domains like medical image processing and robotics, many tasks exist, yet each provides only a handful of labeled examples. His contribution provides a robust framework that exploits shared structure across tasks to enable accurate learning from small, corrupted batches, bridging theory and application. This work has garnered 6 citations and is recognized for its elegant handling of adversarial contamination in a meta-learning setting. Kong’s broader impact includes advancing the theoretical foundations of robust estimation, offering provable guarantees for algorithms that must operate under real-world constraints. For students and researchers, his work exemplifies how rigorous statistical thinking can solve pressing problems in data-limited, safety-critical environments.
Research Focus
Key Achievements
Top Papers
- 1Robust Meta-learning for Mixed Linear Regression with Small Batches6 citations · 2020