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Counterfactually Fair Regression via Optimal Transport

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arXiv:2605. 28251v1 Announce Type: new Abstract: We consider the problem of learning a counterfactually fair regressor.

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Authors: M. Generali Lince, S. Gaucher, J-J. Vie, P. Loiseau
Released: 2026-05-28 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Counterfactually Fair Regression via Optimal Transport overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: under, rate. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.28251

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