simpleMachine
| ML Research
Readings Journey CreditsAbout
I am a third year Statistics PhD student at the University of Chicago. My broad research interest is to understand the empirical phenomena in modern machine learning — where modeling, high-dimensionality, and optimization are intertwined. The goal is to use these insights to derive principled machine learning algorithms. I am currently working on identifying the favorable properties of neural networks that lead to good generalization. I am very fortunate to work with professor Tengyuan Liang. I enjoy simple statements.
The blog posts are meant to summarize some interesting papers I have read, and to share some research thoughts. I hope to bring attention to new and old ideas in the literature of machine learning.
News
- [2020-09-17][Talk][VinAI] Compositional Kernels of Deep Neural Networks
- [2020-05-26][Talk][University of Chicago] Compositional Kernels of Deep Neural Networks
- [2020-04-09][Paper][arXiv] Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
Recent Posts
- [2020-09-22] IMO '20 Problem 6
- [2020-09-18] Stein-Chen Method
- [2020-09-14] Non-asymptotic Concentration of Largest Pairwise Correlation