I am a research scientist in the ByteDance applied machinge learning (AML) research team at Seattle. My research interest is in learning theory, deep learning and optimization. I am also interested in lower bounds and hardness results in computational complexity. Previously, I received Ph.D. in the Paul G. Allen School of Computer Science & Engineering at the University of Washington in 2020. I was fortunate to be advised by Professor Paul Beame and Professor Kevin Jamieson. Before that, I received my B.E. in computer science from IIIS (Yao Class) at Tsinghua University in 2014.
(*alphabetic author order)
Number Balancing is as hard as Minkowski′s Theorem and Shortest Vector, Rebecca Hoberg*, Harishchandra Ramadas*, Thomas Rothvoss*, Xin Yang*, IPCO 2017, Arxiv.
Canaries in the Network, Danyang Zhuo, Qiao Zhang, Xin Yang, Vincent Liu, HotNets 2016.
Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials, Paul Beame*, Shayan Oveis Gharan*, Xin Yang*, COLT 2018, Arxiv.
On the Bias of Reed-Muller Codes over Odd Prime Fields, Paul Beame*, Shayan Oveis Gharan*, Xin Yang*, SIAM Journal on Discrete Mathematics 34 (2), 1232-1247, Arxiv.
Total Least Squares Regression in Input Sparsity Time, Huian Diao*, Zhao Song*, David Woodruff*, Xin Yang*, NeurIPS 2019, Arxiv.
Sketching Transformed Matrices with Applications to Natural Language Processing, Yingyu Liang*, Zhao Song*, Mengdi Wang*, Lin Yang*, Xin Yang*, AISTATS 2020, Arxiv.
Label Leakage and Protection in Two-party Split Learning, Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, Chong Wang, NeurIPS-20 Workshop on Scalability, Privacy, and Security in Federated Learning, Arxiv.