I am a Ph.D. student in Department of Computer Science and Technology at Tsinghua University, advised by Jun Zhu. My research interests are in data-efficient machine learning. I have worked on topics including semi-supervised learning, deep generative models, and approximate inference.

Last semester I was a visiting student at Princeton University working with Ryan P. Adams. In summer 2018 I was a research intern at RIKEN-AIP, Tokyo, working with Masashi Sugiyama and Gang Niu. I also spent time at Google Cloud AI, Beijing as a research intern, supervised by Tomas Pfister. I received my B.E. degree from Department of Electronical Engineering at Tsinghua University in 2015. I also received a Bachelor’s degree in Economics from National School of Development of Peking University.


Publications

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Yucen Luo*, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen*
Under review. Short version accepted at 2nd Symposium on Advances in Approximate Bayesian Inference (AABI).
DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures
Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang
arXiv 1911.09804, 2019.
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation [code]
Zhijie Deng, Yucen Luo, Jun Zhu
International Conference on Computer Vision (ICCV), 2019.
Semi-crowdsourced Clustering with Deep Generative Models [code, poster]
Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang
Advances in Neural Information Processing Systems (NeurIPS), 2018.
Initial version in ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models.
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning [code, poster]
Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Spotlight).
Best paper award at NeurIPS 2017 Workshop on Learning with Limited Labeled Data .
ZhuSuan: A Library for Bayesian Deep Learning [code]
Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou
arXiv 1709.05870, 2017.
Conditional Generative Moment-Matching Networks [code]
Yong Ren, Jialian Li, Yucen Luo, Jun Zhu
Advances in Neural Information Processing Systems (NeurIPS), 2016.