Author

Yu-Hang Jiang

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Jul 2026

Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting

LADDER is proposed, a fixed-model DG pipeline that learns causal/style representations, freezes the encoders, fits source-specific classifiers, and uses an unlabeled target-domain covariate set only at inference to compute weights over these fixed classifiers, with no target labels or model-state updates.

Yu-Hang Jiang, Feng-Chuan Zhang, Sanguo Zhang et al. · arXiv.org · 1 citation

#machine learning Jul 2026

Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting

Domain generalization (DG) aims to learn from multiple source domains and generalize to unseen target domains. Most DG methods pursue invariance: they seek a causal representation whose prediction rule is invariant across domains. This principle is effective when the causal mechanism is stable, but becomes restrictive...

Yuhang Jiang, Feng-Chuan Zhang, Sanguo Zhang et al. · arXiv.org · 0 citations