gamma-star-Y|X Shifts Provide Well-Defined Measure of Distribution Changes Across Domains
A new approach uses entropic optimal transport to define and estimate how covariate and concept shifts differ between source and target domains, even when their supports do not overlap.
English translation prepared automatically for the Brief. Original sources and creator credits remain the reference.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Hongbo Chen, Li Charlie Xia
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers defined a gamma-star-Y|X shift via entropic optimal transport to stay well-defined when source and target covariate supports do not overlap, and they developed estimators and the DataShifts algorithm to quantify shifts from samples.
Why it matters
This offers a practical way to quantify cross-domain changes and bound errors, guiding when transfers are reasonable and when caution is needed.
The work treats covariate and concept shifts as a paired, estimable idea. By using entropic transport, it stays rigorous even if source and target data don’t overlap. The result is a usable bound you can compute from data.
Practically, you can compare different domains fairly, estimate potential error increases, and decide if a transfer effort is worth pursuing or not.
Original sources · 1
- General Quantification of Covariate and Concept Shifts ↗arXiv · 2026-09-10
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Estimators depend on sample quality and chosen beta; high dimensions still pose practical biases and require enough data.
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