Zhixin Zhou

Qualifications

PhD - Statistics (UCLA)

Biography

Dr. Zhou is an Assistant Professor of Management Sciences at the College of Business, City University of Hong Kong. He received his bachelor degree in applied math from UC Berkeley and Ph.D. degree in statistics from UCLA. His current research interest includes random tensor theory, minimax theory of PCA and experimental design on network data. He is also interested in information retrieval, neural ranking and generative adversarial network.

Publications

Journal Publications and Reviews

Zhou, Zhixin; Li, Ping / Rate optimal Chernoff bound and application to community detection in the stochastic block models. March 2020; In: Electronic Journal of Statistics. Vol. 14, No. 1, pp. 1302–1347

Zhou, Zhixin; Amini, Arash / Optimal Bipartite Network Clustering. January 2020; In: Journal of Machine Learning Research. Vol. 21

Zhou, Zhixin; Amini, Arash A. / Analysis of spectral clustering algorithms for community detection: the general bipartite setting. February 2019; In: Journal of Machine Learning Research. Vol. 20

Chapters, Conference Papers, Creative and Literary Works

Ren, Shaogang; Li, Dingcheng; Zhou, Zhixin; Li, Ping / Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection. April 2020; The Web Conference 2020: Proceedings of The World Wide Web Conference WWW 2020. pp. 2287–2297

Tan, Shulong; Zhou, Zhixin; Xu, Zhaozhuo; Li, Ping / Fast Item Ranking under Neural Network based Measures. February 2020; WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining. pp. 591-599

Zhou, Zhixin; Tan, Shulong; Xu, Zhaozhuo; Li, Ping / Möbius Transformation for Fast Inner Product Search on Graph. December 2019; Advances in Neural Information Processing Systems 32 (NIPS 2019).

Tan, Shulong; Zhou, Zhixin; Xu, Zhaozhuo; Li, Ping / On Efficient Retrieval of Top Similarity Vectors. November 2019; Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 5236–5246