Publications

Control and Filtering

• Maximum Correntropy Ensemble Kalman Filter
  Yangtianze Tao , Jiayi Kang, Stephen Shing-Toung Yau
  IEEE Conference on Decision and Control (accepted, CDC 2023).

• Outlier-Robust Iterative Extended Kalman Filtering
  Yangtianze Tao, Stephen Shing-Toung Yau
  IEEE Signal Processing Letters (SPL), 2023.

• Optimal Transportation Particle Filter for Linear Filtering Systems with Correlated Noises
  Jiayi Kang, Xiuqiong Chen, Yangtianze Tao, Stephen Shing-Toung Yau
  IEEE Transactions on Aerospace and Electronic Systems (TAES), 2022.

• The Stochastic Stability Analysis for Outlier Robustness of Kalman-type Filtering Framework Based on Correntropy-Induced Cost
  Yangtianze Tao, Stephen Shing-Toung Yau
  Submitted to IEEE Transactions on Automatic Control (TAC, Full Paper).

• A Uniform Framework of Yau-Yau Algorithm Based on Deep Learning with the Capability of Overcoming the Curse of Dimensionality
  Xiuqiong Chen, Zeju Sun, Yangtianze Tao, Stephen Shing-Toung Yau
  Submitted to IEEE Transactions on Automatic Control (TAC, Full Paper).

• A paper regarding outlier-robust smoothing algorithm for nonlinear systems with non-Gaussian noises
  Yangtianze Tao
  In Preparation.

Machine Learning

• Neural Projection Filter: Learning Unknown Dynamics Driven By Noisy Observations
  Yangtianze Tao, Jiayi Kang, Stephen Shing-Toung Yau
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.

• A paper regarding data-driven state estimatior (supplements for Neural Projection Filter and RNN-based Filter)
  Yangtianze Tao
  In Preparation.

• Double Deep Q-Learning in Opponent Modeling
  Yangtianze Tao, John Doe
  Preprint.

• Bidirectional Recurrent Neural Networks Are Universal Smoothers
  Yangtianze Tao, Xiuqiong Chen, Jiayi Kang, Stephen Shing-Toung Yau
  Submitted to IEEE Transactions on Signal Processing (TSP).

• Recurrent Neural Networks Are Universal Approximators With Stochastic Inputs
  Xiuqiong Chen, Yangtianze Tao, Wenjie Xu, Stephen Shing-Toung Yau
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.

AI for Sciences (Materials Sciences)

• Enhanced Aggregation-Induced Emission Activity of Metal–Organic Frameworks by Using Machine Learning Technology
  Qi Zhang, Yangtianze Tao, Bin Tang, Jin Zhou, Haiyan Wang, Jianmei Wang, Ying Gao, Jianxin Yang, Linhong Ji, Shuangshou Li
  ACS Sustainable Chemistry & Engineering, 2022.

• Graphene Quantum Dots with Improved Fluorescence Activity via Machine Learning: Implications for Fluorescence Monitoring
  Qi Zhang, Yangtianze Tao, Bin Tang, Jianxin Yang, Hanwei Liang, Beibei Wang, Jianmei Wang, Xuan Jiang, Linhong Ji, Shuangshou Li
  ACS Applied Nano Materials, 2022.

• Size-Controllable Eu-MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small-Molecular Identification
  Qi Zhang, Hanwei Liang, Yangtianze Tao, Jianxin Yang, Bin Tang, Rui Li, Yun-Ming Ma, Linhong Ji, Xuan Jiang, Shuangshou Li
  Small Methods, 2022.