Xinwei
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Xinwei Shen

I am a postdoctoral researcher at the Seminar for Statistics, ETH Zürich, working with professors Peter Bühlmann and Nicolai Meinshausen. Previously, I obtained my PhD in the Department of Mathematics at Hong Kong University of Science and Technology in 2022, supervised by professor Tong Zhang. I obtained a Bachelor of Science degree at Fudan University in 2018.

My research interests lie at the interface of statistics and machine learning. My current research focuses on distributional learning, causality, robustness, as well as climate applications.

I will be joining the Department of Statistics at the University of Washington as an assistant professor in fall 2025.

Email  /  Google Scholar  /  Github

Publications

Preprints:

  • Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions
    X. Shen, N. Meinshausen, T. Zhang (2025)
    paper

  • Distributional Instrumental Variable Method
    A. Holovchak, S. Saengkyongam, N. Meinshausen, X. Shen (2025)
    paper

  • Distributional Principal Autoencoders
    X. Shen, N. Meinshausen (2024)
    paper | code | slides

  • Causality-Oriented Robustness: Exploiting General Additive Interventions
    X. Shen, P. Bühlmann, A. Taeb (2023)
    paper | code | slides

  • Asymptotic Statistical Analysis of f-divergence GAN
    X. Shen, K. Chen, T. Zhang (2022)
    paper

  • To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints
    C. Rastogi, I. Stelmakh, X. Shen, M. Meila, F. Echenique, S. Chawla, N. B Shah (2022)
    paper

  • Bidirectional Generative Modeling Using Adversarial Gradient Estimation
    X. Shen, T. Zhang, K. Chen (2020)
    paper | code

    Journal Publications:

  • Engression: Extrapolation through the Lens of Distributional Regression
    X. Shen, N. Meinshausen
    Journal of the Royal Statistical Society Series B, 2024 | paper | code | slides

  • Invariant Probabilistic Prediction
    A. Henzi, X. Shen, M. Law, P. Bühlmann
    Biometrika, 2024 | paper | code | slides

  • Weakly Supervised Disentangled Generative Causal Representation Learning
    X. Shen, F. Liu, H. Dong, Q. Lian, Z. Chen, T. Zhang
    Journal of Machine Learning Research, vol. 23, pp. 1-55, 2022 | paper | code | slides

  • Surprise Sampling: Improving and Extending the Local Case-Control Sampling
    X. Shen, K. Chen, W. Yu
    Electronic Journal of Statistics, vol. 15, pp. 2454-2482, 2021 | paper

  • Phytodiversity is associated with habitat heterogeneity from Eurasia to the Hengduan Mountains
    Y. Chang, K. Gelwick, S. Willett, X. Shen, C. Albouy, A. Luo, Z. Wang, N. Zimmermann, L. Pellissier
    New Phytologist, 2023 | paper

  • In vivo single-cell lineage tracing in zebrafish using high-resolution infrared laser-mediated gene induction microscopy
    S. He, Y. Tian, S. Feng, Y. Wu, X. Shen, K. Chen, Y. He, et al.
    Elife, vol. 9, e52024, 2020 | paper

    Conference Publications:

  • Covariate-Shift Generalization via Random Sample Weighting
    Y. He, X. Shen, R. Xu, T. Zhang, Y. Jiang, W. Zou, P. Cui
    AAAI, 2023 | paper

  • Reframed GES with a Neural Conditional Dependence Measure
    X. Shen, S. Zhu, J. Zhang, S. Hu, Z. Chen
    UAI, 2022 | paper | code

  • TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation
    S. Diao*, X. Shen*, K. S. Shum, Y. Song, T. Zhang
    Findings of ACL, 2021 (*equal contribution) | paper | code

  • CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
    M. Yang, F. Liu, Z. Chen, X. Shen, J. Hao, J. Wang
    CVPR, 2021 | paper | code

    Thesis:

    PhD Thesis: Statistical and Structural Properties of Generative Models

    • Academic Services

  • Co-organizer of the Young Data Science Researcher Seminar Zürich 2022-

  • Workflow Chair of ICML 2021

  • Reviewer of JRSSB, JASA, JMLR, TNNLS, Neural Computation, Machine Learning, Neural Networks, UAI, NeurIPS, ICML, ICLR, CLeaR

    • Industry Experience

  • Research Intern, Huawei Noah's Ark Lab, Hong Kong
    Topic: Causal Machine Learning
    Mentor: Jiji Zhang, Zhitang Chen
    July 2020 - Dec. 2020

  • Research Intern, Beijing Seniverse Technology Co., Ltd, Beijing
    Topic: weather forecasting
    Dec. 2017 - Apr. 2018

    • Teaching

    ETH Zurich:

  • Lecturer of AI Center Projects in Machine Learning Research (Spring 2023)

    Teaching assistant at HKUST:

  • MATH 3424: Regression Analysis (Fall 2020, Spring 2021)

  • MATH 3423: Statistical Inference (Spring 2020)

  • MATH 1013: Calculus IB (Fall 2019)

  • MATH 4426: Survival Analysis (Spring 2019)

    • Awards

  • ETH AI Center Post-Doctoral Fellowship, 2022 - 2023

  • Din-Yu Hsieh Teaching Award, Department of Mathematics, HKUST, 2019 - 2021

  • Postgraduate Scholarship, HKUST, 2018 - 2022

  • Excellent Graduate of Fudan University, 2018

  • China National Scholarship, 2016 - 2017

    • Other Activities

  • Member of HKUST & Fudan Table Tennis Team

  • Member of Fudan Chinese Orchestra

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