About me
I am a Ph.D. candidate at the Center of Foundational Artificial Intelligence of University College London, supervised by
François-Xavier Briol and Arthur Gretton.
I am interested in understanding machine learning algorithms through the dual lenses of optimization and generalization. My current research focuses on generative models, Monte Carlo methods, and causal inference. Prior to my PhD, I obtained my bachelor's degree from the department of Electronic Engineering, Tsinghua University, 2022.
I organize the JumpTrading/ELLIS CSML seminar series on Computational Statistics and Machine Learning for the
UCL ELLIS Unit. Feel free to drop me an email if you would like to give a talk!
Recent News
- 📌 June 2026: 🎤 I will give a talk at MCQMC 2026 in Edinburgh.
- 📌 June 2026: 🏛️ I will give a talk at the UCL Annual Student Conference in the Department of Computer Science.
- 📌 June 2026: 🚆 I will attend ProbAI: Scaling Laws 2026 at Warwick.
- 📌 May 2026: 🎉 Two papers accepted to ICML 2026. One on accelerating kernel-based Wasserstein gradient flows via kernel thinning (paper), and one showing that samples from MMD flow have better cubature properties (paper).
Older news
- 📌 April 2026: I am co-organising UCL IMSS Annual Lecture on Computational Statistics and Machine Learning. This will be followed by London Meeting on Computational Statistics.
- 📌 March 2026: Research visit to Weijie Su at the University of Pennsylvania.
- 📌 March 2026: Research visit to Pradeep Ravikumar at Carnegie Mellon University.
- 📌 March 2026: I am serving as Area Chair for the ICLR 2026 Delta Workshop.
- 📌 November 2025: Two new papers on nonparametric instrumental variables (NPIV)! 🎉 One paper studies convergence guarantees under neural network representations, and the other paper establishes sharp statistical rates for kernel-based estimators.
Selected Publications and Preprints
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(De)-regularized Maximum Mean Discrepancy Gradient FlowZonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. SriperumbudurJMLR '25 Journal of Machine Learning Research
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Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and GeneralizationZonghao Chen, Atsushi Nitanda, Arthur Gretton, Taiji SuzukiPreprint
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Nonparametric Instrumental Variable Regression with Observed CovariatesZikai Shen*, Zonghao Chen*, Dimitri Meunier, Ingo Steinwart, Arthur Gretton†, Zhu Li†Preprint
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Stationary MMD PointsZonghao Chen, Toni Karvonen, Heishiro Kanagawa, François-Xavier Briol, Chris. J. OatesICML '26 International Conference on Machine Learning
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Nested Expectations with Kernel QuadratureZonghao Chen, Masha Naslidnyk, François-Xavier BriolICML '25 International Conference on Machine Learning
Awards
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RIKEN Visiting Fellowship in Machine Learning, RIKEN AIP, Japan
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Newcastle Visiting Fellowship in Machine Learning, Newcastle University
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Tsinghua Presidential Scholarship, Tsinghua University
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Yinghua Scholarship, Tsinghua University