5 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…
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…
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…