This paper considers a particular type of distribution shift, label shift, and develops an e-icient inference procedure for general parameters characterizing the unlabeled target population, and proposes a progressive estimation strategy that unfolds in three stages: an initial heuristic guess, a consistent estimation, and ultimately, an e-icient estimation.
This paper proposes ADA-CS, a plug-and-play module compatible with any ADA or ASFDA framework, and introduces a CSS metric to quantify the Concept Shift Severity across domains, revealing that non-negligible concept shift exists in many transfer tasks.
Cross-fitted estimators that transport information from the two labeled sources through source-specific density ratios are proposed that establish asymptotically linear inference for TPR and FPR, consistency and pointwise inference for the ROC curve, and asymptotically normal inference for AUC.
This work proposes a teacher-student semi-supervised learning framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement, and introduces a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments.
Chikao Tsuchiya, Dhaval Bhanderi, David Ilstrup et al.· 0 citations
Transfer learning improves estimation in a target study using information from related sources. Classical transfer learning is typically data-based, requiring access to the source data or to a model fitted on them. Neither is available in many modern studies, as both are often proprietary or unreported. What can be tra...
The result is a practical way to separate two failure modes that are usually mixed together: a weak selector versus an information channel that cannot support the desired decision in the first place.
We study entity ranking and inference under the Bradley--Terry--Luce (BTL) model using pairwise comparisons collected over sparse comparison graphs. In practice, collecting high-quality human judgments can be expensive and time-consuming, motivating the use of synthetic data augmentation in model training. We analyze a...
Yi-Qiao Jin, Mengxin Yu· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
