Publications

Journal Articles

1. Shengyu Zhu, Biao Chen, Zhitang Chen, and Pengfei Yang, Asymptotically optimal one- and two-sample testing with kernels, IEEE Transactions on Information Theory, to appear, Feburary 2021.
2. Shengyu Zhu and Biao Chen, Distributed detection in ad hoc networks through quantized consensus, IEEE Transactions on Information Theory, vol. 64, no. 11, pp. 7017-7030, November 2018.
3. Shengyu Zhu and Biao Chen, Quantized consensus by the ADMM: Probabilistic versus deterministic quantizers, IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1700-1713, April 2016.
4. Ge Xu, Shengyu Zhu, and Biao Chen, Decentralized data reduction with quantization constraints, IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1700-1713, April 2014. （corresponding author)

Referred Conference Proceedings

1. Shengyu Zhu, Ignavier Ng, and Zhitang Chen, Causal discovery with reinforcement learning, International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. (highest review score and oral presentation; top 1.6%)
2. Shengyu Zhu, Biao Chen, Pengfei Yang, and Zhitang Chen, Universal hypothesis testing with kernels: Asymptotically optimal tests for goodness of fit, International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, April 2019.
3. Shengyu Zhu and Biao Chen, Distributed detection over connected networks via one-bit quantizer, IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, July 2016.
4. Shengyu Zhu and Biao Chen, Distributed average consensus with bounded quantization, IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Edinburgh, UK, July 2016.
5. Shengyu Zhu, Mingyi Hong, and Biao Chen, Quantized consensus ADMM for multi-agent distributed optimization, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016.
6. Shengyu Zhu and Biao Chen, Distributed average consensus with deterministic quantization: an ADMM approach, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, December 2015. (IEEE travel grant)
7. Shengyu Zhu, Ge Xu, and Biao Chen, Are global sufficient statistics always sufficient: the impact of quantization on decentralized data reduction, Asilomar Conference on Signals, Systems, and Computers (Asilomar), Monterey, CA, November 2013. (invited paper)
8. Shengyu Zhu and Biao Chen, Data reduction in tandem fusion systems, IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), Beijing, China, July 2013.
9. Shengyu Zhu, Earnest Akofor, and Biao Chen, Interactive distributed detection with conditionally independent observations, IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, April 2013.

Some Preprints and Workshop Papers

1. Low rank directed acyclic graphs and causal structure learning
2. Masked Gradient-Based Causal Structure Learning
3. A graph autoencoder approach to causal structure learning, NeurIPS Causality Workshop, 2019.