Lecturer
University of Edinburgh
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I am a Lecturer at Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, leading the Edinburgh Ventures for Intelligence and Economics. Within Edinburgh, I am also a Fellow of Edinburgh's Generative AI Laboratory, and an Affiliate of the Institute for Adaptive and Neural Computation, Edinburgh Future Institute, and the Edinburgh Centre for Financial Innovations. Before coming to Edinburgh, I was the Founding Lead of the Trustworthy AI project at JD.com, Inc. I received my BSc in statistics from the University of Science and Technology of China, MPhil and PhD in computer science from the University of Sydney. My research interest is in trustworthy AI, particularly deep learning theory, decentralised learning, privacy and fairness in machine learning, symmetry in machine learning, algorithmic game theory, and their applications in economics and finance. I am an Area Chair of ICML, NeurIPS, UAI, AISTATS, ECAI, and ACML, and an Associate Editor of IEEE Transactions on Technology and Society.
Research Associate post is available: [JD]. Candidates please kindly email me with your CV, transcripts, ans seleted publications.
Fully funded PhD studentships are available from the Centres for Doctoral Training in Machine Learning System, Designing Responsible NLP, and AI for Biomedical Innovation. Visitors are welcome. Candidates please email me, possibly with your CV, transcripts, papers, or anything you are proud of.
How to pronounce my name :)
Any comment is welcome. Please see the full list at my Google Scholar profile.
Wenbin Wu, Kejiang Qian, Alexis Lui, Christopher Jack, Yue Wu, Peter McBurney, Fengxiang He, Bryan Zhang. DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks.
2025. [paper] [Kaggle] [code] [visualisation] NEW
Guanpu Chen, Gehui Xu, Fengxiang He, Dacheng Tao, Thomas Parisini, Karl Henrik Johansson. Inverse learning of black-box aggregator for robust Nash equilibrium.
IEEE Transactions on Automatic Control (TAC), 2025. [paper] NEW
Xuelian Jiang, Tongtian Zhu, Yingxiang Xu, Can Wang, Yeyu Zhang, Fengxiang He, Lie Symmetry Net: Preserving Conservation Laws in Modelling Financial Market Dynamics via Differential Equations.
Transactions on Machine Learning Research (TMLR), 2025. [paper]
Tongtian Zhu, Wenhao Li, Can Wang, Fengxiang He, DICE: Data Influence Cascade in Decentralized Learning.
International Conference on Learning Representations (ICLR), 2025. [paper] [code]
Guanpu Chen, Gehui Xu, Fengxiang He✉, Yiguang Hong, Leszek Rutkowski, and Dacheng Tao, Global Nash Equilibrium in Non-convex Multi-player Game: Theory and Algorithms.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. [paper] [code]
Fengxiang He, Lihao Nan, and Tongtian Zhu, Imagining a Democratic, Affordable Future of Foundation Models: A Decentralised Avenue,
2024. [paper]
Shi Fu, Fengxiang He et al. Convergence of Bayesian Bilevel Optimization.
International Conference on Learning Representations (ICLR), 2024. [paper] [bib]
Tongtian Zhu, Fengxiang He✉ et al. Decentralized SGD and Average-direction SAM are Asymptotically Equivalent.
International Conference on Machine Learning (ICML), 2023. [paper] [code] [bib]
Mengnan Du, Fengxiang He et al. Shortcut Learning of Large Language Models in Natural Language Understanding.
Communications of the ACM, 2023. [paper] [bib]
Tian Qin*, Fengxiang He* et al. “Benefits of Permutation-Equivariance in Auction Mechanisms.” Advances in Neural Information Processing Systems (NeurIPS), 2022. [paper] [bib]
Tongtian Zhu, Fengxiang He✉ et al. Topology-aware Generalization of Decentralized SGD.
International Conference on Machine Learning (ICML), 2022. [paper] [code] [bib]
Shaopeng Fu*, Fengxiang He* et al. Knowledge Removal in Sampling-based Bayesian Inference.
International Conference on Learning Representation (ICLR), 2022. (Part of Shaopeng Fu*, Fengxiang He* et al. Bayesian Inference Forgetting.
) [paper] [code] [bib]
Shaopeng Fu, Fengxiang He et al. Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning.
International Conference on Learning Representations (ICLR), 2022. [paper] [code] [bib]
Fengxiang He*, Bohan Wang* et al. Tighter generalization bounds for iterative differentially private learning algorithms.
Conference on Uncertainty in Artificial Intelligence (UAI), 2021. [paper] [bib]
Zeke Xie, Fengxiang He et al. Artificial neural variability for deep learning: On overfitting, noise memorization, and catastrophic forgetting.
Neural Computation, 2021. [paper] [bib]
Fengxiang He et al. Recent advances in deep learning theory.
2020. [paper] [bib]
Zhuozhuo Tu, Fengxiang He et al. Understanding Generalization in Recurrent Neural Networks.
International Conference on Learning Representation (ICLR), 2020. [paper] [bib]
Fengxiang He*, Bohan Wang* et al. Piecewise linear activations substantially shape the loss surfaces of neural networks.
International Conference on Learning Representations (ICLR), 2020. [paper] [website] [poster] [bib]
Fengxiang He et al. Why ResNet works? Residuals generalize.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2020. [paper] [bib]
Fengxiang He et al. Control batch size and learning rate to generalize well: Theoretical and empirical evidence.
Advances on Neural Information Processing (NeurIPS), 2019. [paper] [poster] [bib]
* Co-first authors.
PhD Students
BrianQian, 2024-present, Assistant Supervisor: Amos Storkey
MScR Students
Interns
PhD Visitor
JudithZhu, 2024-present, from USTC, Co-Supervisor: Miguel de Carvalho
Last update: Fri 9 May 2025