Dr Yongxin Yang

yyang.jpg

CS417

Mile End Road

EECS, QMUL

Yongxin Yang is a Lecturer in Financial Technology at Queen Mary University of London, and he is also a (part-time) Professor in Finance at Southwestern University of Finance and Economics (Chengdu, China). Previously, he was a Lecturer in Machine Learning at University of Surrey.

He received his PhD from QMUL in 2017, supervised by Professor Timothy Hospedales.

His research is in the area of machine learning (transfer learning, domain generalization, and meta learning) and its applications in finance (portfolio optimization and derivatives pricing) and medical genetics.

Apart from being a (rather casual) researcher, he is an ACCA certified accountant and a professional web designer. He co-founded a few companies and holds some consulting roles in industry.

news

Feb 3, 2022 PhenoApt was accepted by AJHG ūüéČ

selected publications

  1. PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning
    Zefu Chen, Yu Zheng, Yongxin Yang, Yingzhao Huang, Sen Zhao, Hengqiang Zhao, Chenxi Yu, and 4 more authors
    The American Journal of Human Genetics, 2022
  2. Augmented sliced Wasserstein distances
    Xiongjie Chen, Yongxin Yang, and Yunpeng Li
    In International Conference on Learning Representations (ICLR), 2022
  3. Loss Function Learning for Domain Generalization by Implicit Gradient
    Boyan Gao, Henry Gouk, Yongxin Yang, and Timothy Hospedales
    In International Conference on Machine Learning (ICML), 2022
  1. Dynamic multi-period sparse portfolio selection model with asymmetric investors’ sentiments
    Ju Wei, Yongxin Yang, Mingzhu Jiang, and Jianguo Liu
    Expert Systems with Applications, 2021
  2. Incorporating Prior Financial Domain Knowledge into Neural Networks for Implied Volatility Surface Prediction
    Yu Zheng, Yongxin Yang, and Bowei Chen
    In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
  3. EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
    Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales
    In Neural Information Processing Systems (NeurIPS), 2021
  1. Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach
    Yu Zheng, Bowei Chen, Timothy M Hospedales, and Yongxin Yang
    In AAAI Conference on Artificial Intelligence (AAAI), 2020
  2. Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
    Wei Zhou, Yiying Li, Yongxin Yang, Huaimin Wang, and Timothy M Hospedales
    In Neural Information Processing Systems (NeurIPS), 2020
  1. Feature-Critic Networks for Heterogeneous Domain Generalization
    Yiying Li, Yongxin Yang, Wei Zhou, and Timothy M Hospedales
    In International Conference on Machine Learning (ICML), 2019
  1. Deep Neural Decision Trees
    Yongxin Yang, Irene Garcia Morillo, and Timothy M Hospedales
    In ICML Workshop on Human Interpretability in Machine Learning (WHI), 2018
  1. Trace Norm Regularised Deep Multi-Task Learning
    Yongxin Yang, and Timothy M Hospedales
    In International Conference on Learning Representations (ICLR) Workshop, 2017
  2. Deep Multi-task Representation Learning: A Tensor Factorisation Approach
    Yongxin Yang, and Timothy Hospedales
    In International Conference on Learning Representations (ICLR), 2017
  3. Gated Neural Networks for Option Pricing: Rationality by Design
    Yongxin Yang, Yu Zheng, and Timothy M Hospedales
    In AAAI Conference on Artificial Intelligence (AAAI), 2017
  1. Multivariate Regression on the Grassmannian for Predicting Novel Domains
    Yongxin Yang, and Timothy M Hospedales
    In Computer Vision and Pattern Recognition (CVPR), 2016
  1. A Unified Perspective on Multi-Domain and Multi-Task Learning
    Yongxin Yang, and Timothy M Hospedales
    In International Conference on Learning Representations (ICLR), 2015