Thesis Information
Title: Using and Saving Randomness
Adviser: David Zuckerman
Institution: University of Texas at Austin
Graduation Date: April 2018
Contact Information
Email Candidate
Candidate Website
SIGACT Membership No.: 7912706
Candidate Bio:
Xue is a postdoc in Northwestern University. Before that, he obtained PHD at University of Texas at Austin, advised by Prof. David Zuckerman. He is broadly interested in randomized algorithms and derandomization. Specific areas include algorithms for big data --- sparse recovery and fast Fourier transform, foundations of machine learning, and pseudorandomness.
Paper 1:
Testing noisy linear functions for sparsity. Xue Chen, Anindya De, and Rocco A. Servedio. In submission, 2019
Link to PDFPaper 2:
Fourier-sparse interpolation without a frequency gap. Xue Chen, Daniel M. Kane, Eric Price, and Zhao Song. FOCS 2016
Link to PDFPaper 3:
Active Regression via Linear-Sample Sparsification. Xue Chen and Eric Price. COLT 2019
Link to PDFKeywords: Algorithms for big data, foundations of machine learning, pseudorandomness, computational complexity