Papers
Research Interests
- Conformal inference
- Causal inference
- Multiple hypothesis testing
- Distributional robustness, generalizability, and replicability
This page organizes my papers by year. See papers by research area here.
(* equal contribution or alphabetical order)
2026
ConfHit: Conformal generative design with oracle-free guarantees
Siddhartha Laghuvarapu, Ying Jin and Jimeng Sun, 2026. OpenReview
International Conference on Learning Representations (ICLR), 2026.
Act or Defer: Error-Controlled Decision Policies for Medical Foundation Models
Ying Jin*, Intae Moon*, and Marinka Zitnik, 2026. medRxiv | Github | Project website
Online selective conformal prediction with asymmetric rules: A permutation test approach
Mingyi Zheng and Ying Jin, 2026. Arxiv | Github
Replicability Within One Study: Harnessing Multiplicity for Observational Causal Inference
Ying Jin
Harvard Data Science Review (Column article), 2026. HDSR
Multi-distribution robust conformal prediction
Yuqi Yang and Ying Jin, 2026. Arxiv | GitHub
2025
Cross-balancing for data-informed design and efficient analysis of observational studies
Ying Jin and José R. Zubizarreta, 2025. Arxiv
ACS: An interactive framework for conformal selection
Yu Gui*, Ying Jin*, Yash Nair*, and Zhimei Ren*, 2025. Arxiv
Diversifying conformal selections
Yash Nair, Ying Jin, James Yang, and Emmanuel Candès, 2025. Arxiv | GitHub
Controllable sequence editing for counterfactual generation
Michelle Li, Kevin Li, Yasha Ektefaie, Ying Jin, Yepeng Huang, Shvat Messica, Tianxi Cai, Marinka Zitnik, 2025.
International Conference on Learning Representations (ICLR), 2026. Arxiv | GitHub
Automated hypothesis validation with agentic sequential falsifications
Kexin Huang*, Ying Jin*, Ryan Li*, Michael Li, Emmanuel Candès, and Jure Leskovec, 2025.
International Conference on Machine Learning (ICML), 2025. Arxiv | GitHub
2024
Beyond reweighting: On the predictive role of covariate shift in effect generalization
Ying Jin, Naoki Egami, and Dominik Rothenhäusler, 2024. Arxiv | GitHub
Proceedings of the National Academy of Sciences (PNAS), 2025.
Optimized Conformal Selection: Powerful selective inference after conformity score optimization
Tian Bai and Ying Jin, 2024. Arxiv | GitHub
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
Confidence on the focal: Conformal prediction with selection-conditional coverage
Ying Jin* and Zhimei Ren*, 2024
Journal of the Royal Statistical Society: Series B (JRSS-B), 2025. Arxiv | GitHub
2023
Diagnosing the role of observable distribution shift in scientific replications
Ying Jin*, Kevin Guo*, and Dominik Rothenhäusler. 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
Biometrika, 2025. Arxiv | software and reproduction code
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
2022
Policy learning “without” overlap: pessimism and generalized empirical Bernstein’s inequality
Ying Jin*, Zhimei Ren*, Zhuoran Yang, and Zhaoran Wang, 2022
Annals of Statistics, 2025. Arxiv | an article on this work | AoS
Modular regression: improving linear models by incorporating auxiliary data
Ying Jin and Dominik Rothenhäusler
Journal of Machine Learning Research (JMLR), 2023. Arxiv | JMLR
Selection by prediction with conformal p-values
Ying Jin and Emmanuel Candès
Journal of Machine Learning Research (JMLR), 2023. Arxiv | Reproduction code | JMLR
Upper bounds on the Natarajan dimensions of some function classes
Ying Jin
IEEE International Symposium on Information Theory (ISIT), 2023. Arxiv | Slides
Sensitivity analysis under the f-sensitivity models: a distributional robustness perspective
Ying Jin*, Zhimei Ren*, and Zhengyuan Zhou
Operations Research, 2025. Arxiv | Journal
Student Paper Award at ICSA Applied Statistics Symposium, 2022.
2021
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
Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021. Arxiv
Tailored inference for finite populations: conditional validity and transfer across distributions
Ying Jin and Dominik Rothenhäusler.
Biometrika, 2023. Arxiv | Software | Journal
2020
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