Jie Xu

Jie Xu

Ph.D, Assistant Professor, Department of Health Outcomes and Biomedical Informatics, University of Florida

Publications

Federated Learning Similarity Learning Super-resoluton Imaging

Federated Learning & Healthcare

Federated learning (aka collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging their data samples.

  • Jie Xu, Zhenxing Xu, Peter Walke, Fei Wang. Federated Patient Hashing. The Thirty-Fourth AAAI Con-ference on Artificial Intelligence (AAAI-20), 2020.
  • Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang. Federated Learning for Healthcare Informatics. Journal of Healthcare Informatics Research (JHIR), 2020.
  • Vaid, Akhil, Suraj K. Jaladanki, Jie Xu, Shelly Teng, Arvind Kumar, Samuel Lee, Sulaiman Somani et al. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach. JMIR medical informatics, 2021.
  • Jie Xu, Wei Zhang, and Fei Wang. A (DP) $^ 2$ SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy. arXiv preprint arXiv:2008.09246, 2020.
  • Jie Xu, Fei Wang, Zhenxing Xu, Prakash Adekkanattu, Pascal Brandt, Guoqian Jiang, Richard C. Kiefer et al. Data‐driven discovery of probable Alzheimer’s disease and related dementia subphenotypes using electronic health records. Learning Health Systems, 2020.

Similarity Learning

Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn from a similarity function that measures how similar or related two objects are.

  • Lei Luo, Jie Xu, Cheng Deng, Heng Huang. Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019.
  • Lei Luo, Jie Xu, Cheng Deng, Heng Huang. Orthogonality-Promoting Dictionary Learning via Bayesian Inference. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019.
  • Jie Xu, Lei Luo, Cheng Deng, Heng Huang. Bilevel Distance Metric Learning for Robust Image Recognition. Thirty-second Conference on Neural Information Processing Systems (NIPS), 2018.
  • Jie Xu, Lei Luo, Cheng Deng, Heng Huang. New Robust Metric Learning Model Using Maximum Correntropy Criterion. SIGKDD: The community for data mining, data science and analytics (SIGKDD), 2018, 2555-2564, London, United Kingdom, 2018.8.19-8.23.
  • Jie Xu, Lei Luo, Cheng Deng, Heng Huang. Multi-Level Metric Learning via Smoothed Wasserstein Distance. International Joint Conference on Artificial Intelligence (IJCAI), 2018, Stockholm, Sweden, 2018.7.13-7.19.
  • Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang. Predicting Alzheimer’s Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model. 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017, 3880-3886, Melbourne, Australia, 2017.8.17-8.25.
  • Jie Xu, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, Heng Huang*. Multi-Class Support Vector Machine via Maximizing Multi-Class Margins. 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017, 3154-3160, Melbourne, Australia, 2017.8.17-8.25.

Super-resoluton Imaging

Super-resolution imaging (SR) is a class of techniques that enhance (increase) the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced.

  • Cheng Deng, Jie Xu, Kaibing Zhang, Dacheng Tao, Xinbo Gao, Xuelong Li. Similarity Constraints Based Structured Output Regression Machine: An Approach to Image Super-Resolution. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(12): 2472-2485. (JCR-I, IF = 6.108)
  • Xinxia Fan, Yanhua Yang, Cheng Deng, Jie Xu, Xinbo Gao. Compressed Multi-scale Feature Fusion Network for Single Image Super-Resolution. Signal Processing, 2017. (JCR-II, IF = 3.110)
  • Jie Xu, Cheng Deng*, Xinbo Gao, Dacheng Tao, Xuelong Li. Image super-resolution using multi-layer support vector regression. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, 5799-5803, Florence, Italy, 2014.5.4-5.9.
  • Jie Xu, Cheng Deng, Xianglong Liu, Jie Li. Image Super-resolution Based on Sparse Representation With Joint Constraints. International Conference on Internet Multimedia Computing and Service (ICIMCS), 2014, 381-385, Xiamen, China, 2014.7.10-7.12.
  • Songhang Ye, Cheng Deng, Jie Xu, Xinbo Gao. Coupled Fisher Discrimination Dictionary Learning for Single Image Super-resolution. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2015, 1196-1200, Queensland, Australia, 2015.4.19-2015.4.24.
  • Fang Xie, Cheng Deng, Jie Xu, Jifei Yu, Jie Li. Image Super Resolution Using Gaussian Process Regression with Patch Clustering. International Conference on Internet Multimedia Computing and Service (ICIMCS), 2013, 109-112, Huang shan, China, 2013.8.17-8.18.