Chen Dan 丹晨
I am a 5th year Ph.D. student at Computer Science Department, Carnegie Mellon University, advised by Pradeep Ravikumar (previously also co-advised by Avrim Blum). Prior to this, I received B.Sc from School of EECS, Peking University, where I worked with Liwei Wang. My research interest is in the theoretical aspect of machine learning and algorithm design.
I visited Toyota Technological Institute at Chicago in summer 2018, hosted by Avrim Blum.
My CV can be found here (last update: June 2020).
Email: cdan at cs dot cmu dot edu ; You may also find me on Google Scolar, DBLP, and Twitter.
- [February 2021] I will be TA'ing Practical Data Science in the spring 2021 semester (Instructor: Zico Kolter).
- [January 2021] Our paper Learning Complexity of Simulated Annealing has been accepted by AISTATS 2021. arXiv
- [May 2020] Our paper Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification has been accepted by ICML 2020. arXiv
- [May 2020] Our paper Class-Weighted Classification: Trade-offs and Robust Approaches has been accepted by ICML 2020. arXiv
- [January 2020] Our paper Learning Sparse Nonparametric DAGs has been accepted by AISTATS 2020. arXiv
- [December 2019] Our paper MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius has been accepted by ICLR 2020. OpenReview
- [September 2019] Our paper Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation has been accepted by NeurIPS 2019. arXiv, Poster