144 citations · 256 across the 17 of their papers we have counts for
17 papers
Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
Ying Jin, Dominik Rothenhäusler
Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another. This str…
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
Ziang Song, Ying Jin, Emmanuel J. Candès
Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-v…
Automated Hypothesis Validation with Agentic Sequential Falsifications
Kexin Huang, Ying Jin, Ryan Li +3
Hypotheses are central to information acquisition, decision-making, and discovery. However, many real-world hypotheses are abstract, high-level statements that are difficult to val…
Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization
Ying Jin, Naoki Egami, Dominik Rothenhäusler
Many existing approaches to generalizing statistical inference amidst distribution shift operate under the covariate shift assumption, which posits that the conditional distributio…
Diagnosing the role of observable distribution shift in scientific replications
Ying Jin, Kevin Guo, Dominik Rothenhäusler
Many researchers have identified distribution shift as a likely contributor to the reproducibility crisis in behavioral and biomedical sciences. The idea is that if treatment effec…
Model-free selective inference under covariate shift via weighted conformal p-values
Ying Jin, Emmanuel J. Candès
This paper introduces novel weighted conformal p-values and methods for model-free selective inference. The problem is as follows: given test units with covariates and missing…
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Kexin Huang, Ying Jin, Emmanuel Candès +1
Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable de…
Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
Ying Jin, Zhimei Ren, Zhuoran Yang +1
This paper studies offline policy learning, which aims at utilizing observations collected a priori (from either fixed or adaptively evolving behavior policies) to learn an optimal…
Modular Regression: Improving Linear Models by Incorporating Auxiliary Data
Ying Jin, Dominik Rothenhäusler
This paper develops a new framework, called modular regression, to utilize auxiliary information -- such as variables other than the original features or additional data sets -- in…
Selection by Prediction with Conformal p-values
Ying Jin, Emmanuel J. Candès
Decision making or scientific discovery pipelines such as job hiring and drug discovery often involve multiple stages: before any resource-intensive step, there is often an initial…
Sensitivity analysis under the -sensitivity models: a distributional robustness perspective
Ying Jin, Zhimei Ren, Zhengyuan Zhou
This paper introduces the -sensitivity model, a new sensitivity model that characterizes the violation of unconfoundedness in causal inference. It assumes the selection bias due…
Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach
Ying Jin, Zhimei Ren, Emmanuel J. Candès
We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports…
Towards Optimal Variance Reduction in Online Controlled Experiments
Ying Jin, Shan Ba
We study optimal variance reduction solutions for count and ratio metrics in online controlled experiments. Our methods leverage flexible machine learning tools to incorporate cova…
Contemporary Symbolic Regression Methods and their Relative Performance
William La Cava, Patryk Orzechowski, Bogdan Burlacu +5
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent…
Tailored inference for finite populations: conditional validity and transfer across distributions
Ying Jin, Dominik Rothenhäusler
Parameters of sub-populations can be more relevant than super-population ones. For example, a healthcare provider may be interested in the effect of a treatment plan for a specific…
Is Pessimism Provably Efficient for Offline RL?
Ying Jin, Zhuoran Yang, Zhaoran Wang
We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. Due to the lack of further interactions with the environm…
Bayesian Symbolic Regression
Ying Jin, Weilin Fu, Jian Kang +2
Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machi…