preprints

  1. Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values Guo, F. Richard, and Rajen D. Shah 2023 [Abs] [arXiv] [Code]
  2. Confounder selection: objectives and approaches Guo, F. Richard, Anton Rask Lundborg, and Qingyuan Zhao 2022 [Abs] [arXiv]
  3. Empirical Bayes for large-scale randomized experiments: a spectral approach Guo, F. Richard, James McQueen, and Thomas S. Richardson 2020 [Abs] [arXiv]

journals

  1. Variable elimination, graph reduction and efficient g-formula Guo, F. Richard, Emilija Perković, and Andrea Rotnitzky Biometrika 2022+ [Abs] [arXiv] [HTML] [Poster] [Slides] [Code]
  2. BSDE: Barycenter single-cell differential expression for case-control studies Mengqi Zhang, and Guo, F. Richard Bioinformatics 2022 [Abs] [HTML] [Code]
  3. Efficient least squares for estimating total effects under linearity and causal sufficiency Guo, F. Richard, and Emilija Perković Journal of Machine Learning Research 2022 [Abs] [arXiv] [HTML] [Slides] [Code]
  4. Chernoff-type concentration of empirical probabilities in relative entropy Guo, F. Richard, and Thomas S. Richardson IEEE Transactions on Information Theory 2021 [Abs] [arXiv] [HTML] [Code]
  5. Discussion of ’Estimating time-varying causal excursion effect in mobile health with binary outcomes’ Guo, F. Richard, Thomas S. Richardson, and James M. Robins Biometrika 2021 [Abs] [arXiv] [HTML]
  6. On testing marginal versus conditional independence Guo, F. Richard, and Thomas S. Richardson Biometrika 2020 [Abs] [arXiv] [HTML] [Slides]
  7. How cognitive and reactive fear circuits optimize escape decisions in humans Song Qi, Demis Hassabis, Jiayin Sun, Guo, Fangjian, Nathaniel Daw, and Dean Mobbs Proceedings of the National Academy of Sciences (PNAS) 2018 [Abs] [HTML]
  8. Bounds of memory strength for power-law series Guo, Fangjian, Dan Yang, Zimo Yang, Zhi-Dan Zhao, and Tao Zhou Physical Review E 2017 [arXiv] [HTML]

conferences

  1. Minimal enumeration of all possible total effects in a Markov equivalence class Guo, F. Richard, and Emilija Perković In AISTATS 2021 [Abs] [arXiv] [HTML] [Poster]
  2. Boosting variational inference Guo, Fangjian, X Wang, K Fan, T Broderick, and D Dunson In NIPS Workshop on Advances in Approximate Bayesian Inference 2016 [Abs] [arXiv] [HTML] [Code]
  3. The Bayesian Echo Chamber: modeling social influence via linguistic accommodation Guo, Fangjian, Charles Blundell, Hanna Wallach, and Katherine Heller In AISTATS 2015 [arXiv] [HTML] [Code]
  4. Uncovering systematic bias in ratings across categories: a Bayesian approach Guo, Fangjian, and David Dunson In RecSys 2015 [HTML]
  5. Parallelizing MCMC with random partition trees Xiangyu Wang, Guo, Fangjian, Katherine Heller, and David Dunson In NIPS 2015 [Abs] [arXiv] [HTML] [Code]

PhD thesis

  1. Likelihood analysis of causal models Guo, F. Richard 2021 [Abs] [HTML]

notes and expository writings

  1. Causal Inference by using invariant prediction by Peters, Buhlmann and Meinshausen (2016) Guo, F. Richard 2018 [HTML] [Code]