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TILT: Target-induced loss tilting under covariate shift

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arXiv:2605. 14280v1 Announce Type: cross Abstract: We introduce and analyze Target-Induced Loss Tilting (TILT) for unsupervised domain adaptation under covariate shift.

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Authors: Kakei Yamamoto, Martin J. Wainwright
Released: 2026-05-15 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because TILT: Target-induced loss tilting under covariate shift 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 "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (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)". Matching terms: under, source, line, control. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.14280

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