Chen Xu's Personal Webpage

A picture of me

I am a Research Scientist at Toyota Research Institute (Large Behavior Models division).
Education:
--- I completed my PhD in Operations Research (specializing in Machine Learning), working with Prof. Yao Xie at Georgia Tech ISyE [Thesis Defense slides].
--- Before my PhD, I completed my joint BS/MS degrees, working with 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. TRI LBM team. A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation [Website]. arXiv:2507.05331, 2025.
  2. 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 [Website, Poster, RSS Talk]. Robotics: Science and Systems (RSS) 2025.

Flow-based Generative Modeling

See this introductory slide to normalizing flow and flow matching, 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. Flow-based conformal prediction for multi-dimensional time series.. arXiv:2502.05709, 2025.
  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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