Dr Yongxin Yang
CS417
Mile End Road
EECS, QMUL
Yongxin Yang is a Lecturer in Financial Technology at Queen Mary University of London. 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 🎉 |
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selected publications
- MEDFAIR: Benchmarking Fairness for Medical ImagingIn International Conference on Learning Representations (ICLR), 2023
- Mixture of Normalizing Flows for European Option PricingIn Conference on Uncertainty in Artificial Intelligence (UAI), 2023
- On Calibration of Mathematical Finance Models by HypernetworksIn European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2023
- PhenoApt leverages clinical expertise to prioritize candidate genes via machine learningThe American Journal of Human Genetics, 2022
- Augmented sliced Wasserstein distancesIn International Conference on Learning Representations (ICLR), 2022
- Loss Function Learning for Domain Generalization by Implicit GradientIn International Conference on Machine Learning (ICML), 2022
- Dynamic multi-period sparse portfolio selection model with asymmetric investors’ sentimentsExpert Systems with Applications, 2021
- Incorporating Prior Financial Domain Knowledge into Neural Networks for Implied Volatility Surface PredictionIn ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
- EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationIn Neural Information Processing Systems (NeurIPS), 2021
- Index Tracking with Cardinality Constraints: A Stochastic Neural Networks ApproachIn AAAI Conference on Artificial Intelligence (AAAI), 2020
- Online Meta-Critic Learning for Off-Policy Actor-Critic MethodsIn Neural Information Processing Systems (NeurIPS), 2020
- Feature-Critic Networks for Heterogeneous Domain GeneralizationIn International Conference on Machine Learning (ICML), 2019
- Deep Neural Decision TreesIn ICML Workshop on Human Interpretability in Machine Learning (WHI), 2018
- Trace Norm Regularised Deep Multi-Task LearningIn International Conference on Learning Representations (ICLR) Workshop, 2017
- Deep Multi-task Representation Learning: A Tensor Factorisation ApproachIn International Conference on Learning Representations (ICLR), 2017
- Gated Neural Networks for Option Pricing: Rationality by DesignIn AAAI Conference on Artificial Intelligence (AAAI), 2017
- Multivariate Regression on the Grassmannian for Predicting Novel DomainsIn Computer Vision and Pattern Recognition (CVPR), 2016
- A Unified Perspective on Multi-Domain and Multi-Task LearningIn International Conference on Learning Representations (ICLR), 2015