Papers

Preprints

• Confidence on the focal: Conformal prediction with selection-conditional coverage
  Ying Jin* and Zhimei Ren*, 2024. Arxiv

• Diagnosing the role of observable distribution shift in scientific replications
  Ying Jin*, Kevin Guo*, and Dominik Rothenhäusler, 2023.
  Arxiv | awesome-replicability-data | R package | shiny app

• Model-free selective inference under covariate shift via weighted conformal p-values
  Ying Jin and Emmanuel Candès, 2023.
  Arxiv | software and reproduction code

• Policy learning “without” overlap: pessimism and generalized empirical Bernstein’s inequality
  Ying Jin*, Zhimei Ren*, Zhuoran Yang, and Zhaoran Wang, 2022.
  Arxiv | Fun read: an article on this work

• Sensitivity analysis under the f-sensitivity models: a distributional robustness perspective
  Ying Jin*, Zhimei Ren*, and Zhengyuan Zhou, 2022. Arxiv
  Student Paper Award at ICSA Applied Statistics Symposium, 2022.

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Publications

• Conformal alignment: knowing when to trust foundation models with guarantees
  Yu Gui*, Ying Jin* and Zhimei Ren*, 2024.
  Conference on Neural Information Processing Systems (NeurIPS), 2024. Arxiv | GitHub

• Uncertainty quantification over graph with conformalized graph neural networks
  Kexin Huang, Ying Jin, Emmanuel Candès, and Jure Leskovec.
  Conference on Neural Information Processing Systems (NeurIPS), 2023 (Spotlight). Arxiv | GitHub

• Modular regression: improving linear models by incorporating auxiliary data
  Ying Jin and Dominik Rothenhäusler.
  Journal of Machine Learning Research (JMLR), 2023. Arxiv

• Selection by prediction with conformal p-values
  Ying Jin and Emmanuel Candès.
  Journal of Machine Learning Research (JMLR), 2023. Arxiv | Reproduction code

• Upper bounds on the Natarajan dimensions of some function classes
  Ying Jin.
  IEEE International Symposium on Information Theory (ISIT), 2023. Arxiv | Slides

• Tailored inference for finite populations: conditional validity and transfer across distributions
  Ying Jin and Dominik Rothenhäusler.
  Biometrika, 2023. Arxiv | Software

• Sensitivity analysis of individual treatment effects: a robust conformal inference approach
  Ying Jin*, Zhimei Ren*, and Emmanuel Candès.
  Proceedings of the National Academy of Sciences (PNAS), 2023.
  Arxiv | Software | Reproduction code | Website | OCIS talk | Commentary from Chernozhukov et al.
  Runner up for Tom Ten Have Award at American Causal Inference Conference (ACIC), 2022.

• Towards optimal variance reduction in online controlled experiments
  Ying Jin and Shan Ba. (2021 summer internship project at LinkedIn)
  Technometrics, 2023. Arxiv
  2024 Jack Youden Prize for the best expository paper in Technometrics.

• Contemporary symbolic regression methods and their relative performance
  William La Cava, P. Orzechowski, B. Burlacu, F. Olivetti de Franca, M. Virgolin, Ying Jin, M. Kommenda and J. Moore, 2021.
  Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021. Arxiv

• Is pessimism provably efficient for offline RL?
  Ying Jin, Zhuoran Yang, and Zhaoran Wang.
  Mathematics of Operations Research, 2024+. Short version in ICML 2021. MathOR | Arxiv | RL seminar talk | Slides

(* equal contribution or alphabetical order)

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Undergraduate works

• Computational-statistical tradeoffs in inferring combinatorial structures of Ising model
  Ying Jin, Zhaoran Wang, and Junwei Lu.
  International Conference on Machine Learning (ICML), 2020. PMLR

• Bayesian symbolic regression
  Ying Jin, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo. (undergrad internship project)
  Proceedings of the 9th International Workshop on Statistical Relational Artificial Intelligence, 2020. Arxiv