Research Interests:

Machine learning theory; probabilistic graphical models; high-dimensional signal and information processing; and physics-inspired optimization algorithms.

Working topics:

1. Theoretical aspects of machine learning with a special interest in the interdisciplinary field between high-dimensional statistics and statistical physics;

2. Online learning and continuous learning;

3. Graph neural networks and probabilistic graphical models.

   

081104-模式识别与智能系统
081002-信号与信息处理
070201-理论物理

机器学习理论
特征表示学习
概率图模型与图神经网络

中国科学院自动化研究所，副研究员，2019-08-23 至今

哈佛大学 工程与应用科学学院 电子工程方向，Research Associate，2018-02-01 至 2019-08-31

哈佛大学  工程与应用科学学院  电子工程方向，Postdoctoral Fellow，015-02-01 至 2018-01-31

中国科学院理论物理研究所，博士，2010-09-01 至 2015-01-31

东北师范大学，学士，2006-09-01 至 2010-08-31

机器学习
机器学习导论

机器学习 2021年春

Conference:

[C.7] Chuang Wang, Hong Hu, Yue M. Lu, A Solvable High-Dimensional Model of GAN, Advances in Neural Information Processing Systems (NeurIPS), 2019

[C.6] Chuang Wang, Yue M. Lu, The scaling limit of high-dimensional online independent component analysis, Advances in Neural Information Processing Systems 

(NIPS), 2017 (Spotlight talk)

[C.5] Chuang Wang, Yonina C. Eldar and Yue M. Lu, Subspace estimation from incomplete observations: a precise high-dimensional analysis, 

Signal Processing with Adaptive Sparse Structured Representations (SPARS), Lisbon, Portugal, 2017 (Oral talk)

[C.4] Chuang Wang, Yue M. Lu, Online learning for sparse PCA in high dimensions: exact dynamics and phase transitions, 

2016 IEEE Information Theory Workshop (ITW), 186-190, 2016

[C.3] Chuang Wang, A. Agaskar and Yue M. Lu, "Randomized Kaczmarz algorithm for inconsistent linear systems: an exact MSE analysis," 

International Conference on Sampling Theory and Applications (SampTA), Washington, DC, 2015, pp. 498-502.

[C.2] A. Agaskar, Chuang Wang, Yue M. Lu, Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilities, 

IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014 (Best Student Award Paper)

[C.1] Chuang Wang, Haijun Zhou, Simplifying generalized belief propagation on redundant region graphs, 

Journal of Physics: Conference Series, 473, 012004 (2013)

Journal:

[J.8] Chuang Wang, Yue M. Lu, The scaling limit of high-dimensional online independent component analysis, 

Journal of Statistical Mechanics: Theory and Experiment, 2019 (accepted, JCR Q1)

[J.7] Chuang Wang, Yonina C. Eldar, Yue M. Lu, Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis, 

IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, pp. 1240-1252, 2018 (JCR Q1)

[J.6] Chuang Wang, Jonathan Mattingly, Yue M. Lu, Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to 

Regularized Regression and PCA, 2017, arXiv:1712.04332 (Preprint)

[J.5] G. D. Ferraro, Chuang Wang, Haijun Zhou, E. Aurell, On one-step replica symmetry breaking in the Edwards-Anderson spin glass model, 

Journal of Statistical Mechanics: Theory and Experiment, vol.7, pp.073305 (JCR Q1)

[J.4] Chuang Wang, Shaomeng Qin, Haijun Zhou, Topologically invariant tensor renormalization group method for the Edwards-Anderson spin glasses model, 

Physical Review B 90, vol.17, pp. 174201, 2014 (JCR Q1)

[J.3] Haijun Zhou, Chuang Wang, Region graph partition function expansion and approximate free energy landscapes: Theory and some numerical results, 

Journal of Statistical Physics, vol. 148, pp. 513, 2012 (JCR Q2)

[J.2] Haijun Zhou, Chuang Wang, Zedong Bi, Jinqing Xiao, Partition function expansion on region graphs and message-passing equations, 

Journal of Statistical Mechanics: Theory and Experiment, vol.12., pp. L12001 (JCR Q1)

[J.1] Haijun Zhou, Chuang Wang, Ground-state configuration space heterogeneity of random finite-connectivity spin glasses and random constraint satisfaction 

problems, Journal of Statistical Mechanics: Theory and Experiment, vol 10, pp. P10010, 2010 (JCR Q1)

   

2019 刘文卓 （已转博：导师刘成林）

2020 孟令寰

2021 王天

2022 宋思清