Chen Xu's Personal Webpage

A picture of me

I am a Research Scientist at Toyota Research Institute (Large Behavior Models).
Education:
--- I completed my PhD in Operations Research (specializing in Machine Learning) under the supervision of Prof. Yao Xie at Georgia Tech ISyE [Thesis Defense slides].
--- Before my PhD, I completed my joint BS/MS degrees under the supervision of Prof. Rina Foygel Barber at the University of Chicago. My MS degree is in Statistics [Thesis link], and my BS degrees are Computational and Applied Mathematics and Economics (data science concentration).
Email: cxu310@gatech.edu

Websites: [LinkedIn, Google Scholar, GitHub]

Research Interests: flow-based generative modeling, safe & reliable machine learning.

Research

Imitation Learning in Robotics

  1. Chen Xu, T. K. Nguyen, P. Miller, R. Lee, P. Shah, R. A. Ambrus, H. Nishimura, and M. Itkina. Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies. Robotics: Science and Systems (RSS) 2025.

Flow-based Generative Modeling

See this introductory slide to normalizing flow and flow matcing, where I presented at Toyota Research Institute Machine Learning reading group.

  1. Chen Xu, Xiuyuan Cheng, and Yao Xie. Local flow matching generative models. arXiv:2410.02548, 2024.
  2. Chen Xu, Jonghyeok Lee, Xiuyuan Cheng, and Yao Xie. Flow-based distributionally robust optimization. IEEE Journal on Selected Areas in Information Theory (JSAIT), 2024. [Poster]
  3. Chen Xu, Xiuyuan Cheng, and Yao Xie. Normalizing flow neural networks by jko scheme. NeurIPS 2023 (Spotlight). [Poster, Slide]
  4. Chen Xu, Xiuyuan Cheng, and Yao Xie. Computing high-dimensional optimal transport by flow neural networks. AISTATS 2025. [Poster]
  5. Chen Xu, Xiuyuan Cheng, and Yao Xie. Invertible neural networks for graph prediction. IEEE Journal on Selected Areas in Information Theory (JSAIT), 2022. [Poster, Slide]

Conformal Prediction

See this slide as a summary of my progress (up to the end of 2024) on time-serie conformal prediction, with a recorded talk organized by Time Series Analysis And Forecasting Society (TAFS).

  1. Junghwan Lee, Chen Xu, and Yao Xie. Transformer Conformal Prediction for Time Series. ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling.
  2. Jonghyeok Lee, Chen Xu, and Yao Xie. Kernel-based optimally weighted conformal prediction intervals. ICLR 2025.
  3. Chen Xu, Hanyang Jiang, and Yao Xie. Conformal prediction for multi-dimensional time series by ellipsoidal sets. ICML 2024 (Spotlight). [Poster, Slide]
  4. Chen Xu and Yao Xie. Sequential predictive conformal inference for time series. ICML 2023. [Poster, Slide]
  5. Chen Xu and Yao Xie. Conformal prediction for time series. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. [Poster, Slide]
    6. - The method EnbPI has been independently implemented in six open-source libraries: MAPIE, Fortuna, skpro, TorchCP, PUNCC, ConformalPrediction.jl.
  6. Chen Xu and Yao Xie. Conformal prediction set for time-series. ICML 2022 Workshop on Distribution-free Uncertainty Quantification. [Poster]
  7. Chen Xu and Yao Xie. Conformal anomaly detection on spatio-temporal observations with missing data. ICML 2021 Workshop on Distribution-free Uncertainty Quantification. [Poster]
  8. Chen Xu and Yao Xie. Conformal prediction interval for dynamic time-series. ICML 2021 (oral).
  9. Byol Kim, Chen Xu, and Rina Barber. Predictive inference is free with the jackknife+-after-bootstrap. NeurIPS 2020.

Miscellaneous

  1. Chen Xu, Minghe Zhang, Andy Sun, Feng Qiu, and Yao Xie. Solar radiation ramping events modeling using spatio-temporal point processes. INFORMS Journal on Data Science (IJDS), 2025. [Slide]
  2. Chen Xu, Yao Xie, Daniel A Zuniga Vazquez, Rui Yao, and Feng Qiu. Spatio-temporal wildfire prediction using multi-modal data. IEEE Journal on Selected Areas in Information Theory (JSAIT), 2023. [Slide]
  3. Anatoli Juditsky, Arkadi Nemirovski, Yao Xie, and Chen Xu. Generalized generalized linear models: Convex estimation and online bounds. arXiv:2304.13793, 2023.
  4. Chen Xu, Xiuyuan Cheng, and Yao Xie. An alternative approach to train neural networks using monotone variational inequality. OPT 2023: Optimization for Machine Learning. [Poster]

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