Research Interests

• Conformal prediction
• Multiple hypothesis testing
• Causal inference
• Data-driven decision making
• Distributional robustness, generalizability, and replicability

Preprints

• Conformal alignment: knowing when to trust foundation models with guarantees
  Yu Gui*, Ying Jin*, and Zhimei Ren*, 2024.

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

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

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

• Policy learning “without” overlap: pessimism and generalized empirical Bernstein’s inequality
  Ying Jin*, Zhimei Ren*, Zhuoran Yang, and Zhaoran Wang, 2022.
  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.
  Student Paper Award at ICSA Applied Statistics Symposium, 2022.

Publications

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

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

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

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

• Tailored inference for finite populations: conditional validity and transfer across distributions
  [software: condinf, transinf] [JSM talk] [slides]
  Ying Jin and Dominik Rothenhäusler.
  Biometrika, 2023.

• Sensitivity analysis of individual treatment effects: a robust conformal inference approach
  [software] [reproduction code] [website] [OCIS talk] [commentary from Chernozhukov et al.]
  Ying Jin*, Zhimei Ren*, and Emmanuel Candès.
  Proceedings of the National Academy of Sciences (PNAS), 2023.
  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, 2022.

• Contemporary symbolic regression methods and their relative performance
  William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabricio Olivetti de Franca, Marco Virgolin, Ying Jin, Michael Kommenda and Jason H. Moore, 2021. (software contribution for Bayesian Symbolic Regression)
  Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021.

• Is pessimism provably efficient for offline RL? [talk at RL theory seminar] [slides]
  Ying Jin, Zhuoran Yang, and Zhaoran Wang.
  International Conference on Machine Learning (ICML), 2021.

(* equal contribution or alphabetical order)

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.

• 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.