Jianfei Chen

Associate Professor

Email: jianfeic@mail.tsinghua.edu.cn

Homepage: https://ml.cs.tsinghua.edu.cn/~jianfei/

Education Background

Bachelor of Computer Science, Tsinghua University, China, 2014

Ph.D. in Computer Science, Tsinghua University, China, 2019

Research Interests

Machine learning, Deep learning, Bayesian methods

Research Status

Research work focuses on efficient algorithms and basic theories for deep learning. Aiming at the problems of large amount of computation, slow convergence speed and high memory consumption of deep learning algorithms, I proposed several efficient algorithms with theoretical guarantees: (1) fast and memory-saving neural network training algorithm based on random quantization; (2) Efficient machine learning algorithms based on random sampling and variance reduction; (3) Efficient algorithms for deep generative models; (4) Efficient algorithms and large-scale training systems for topic models. The research results are released as open source software such as ActNN, WarpLDA, and ZhuSuan. The above achievements have been published in more than 20 papers in the top international conferences on machine learning such as NeurIPS, ICML, and ICLR. The research work is supported by the Youth Fund of Natural Science Foundation of China.

Academic Achievement

[1] Jianfei Chen*, Lianmin Zheng*, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, and Joseph E. Gonzalez. “ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training”. In ICML. 2021.

[2] Jianfei Chen, Gai Yu, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez. “A Statistical Framework for Low-bitwidth Training of Deep Neural Networks”. In NeurIPS. 2020.

[3] Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian. “VFlow: More Expressive Generative Flows with Variational Data Augmentation”. In ICML. 2020.

[4] Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang. “Stochastic Expectation Maximization with Variance Reduction.” In NIPS. 2018.

[5] Jianfei Chen, Jun Zhu, Le Song. "Stochastic training of graph convolutional networks with variance reduction." In ICML. 2018.

[6] Jianfei Chen, Jun Zhu, Jie Lu, Shixia Liu. "Scalable Inference for Nested Chinese Restaurant Process Topic Models." In VLDB. 2018.

[7] Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu. “SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs.” In ASPLOS. 2017.

[8] Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang. "Population matching discrepancy and applications in deep learning." In NeurIPS. 2017.

[9] Jianfei Chen, Kaiwei Li, Jun Zhu, Wenguang Chen. “WarpLDA: a Cache Efficient O (1) Algorithm for Latent Dirichlet Allocation.” In VLDB. 2016.

[10] Jianfei Chen, Xun Zheng, Zi Wang, Jun Zhu, Bo Zhang. “Scalable Inference for Logistic-Normal Topic Models.” In NIPS. 2013.