arXiv:2603.15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes. Existing proxy-based approaches that address latent shift rely on a strong completeness assumption to uniquely determine (point-identify) a robust predictor. Completeness requires that proxies have sufficient information about variations in latent confounders. For imperfect proxies the mapping from confounders to the space of proxy distributions is non-injective, and m
This paper addresses a fundamental challenge in domain adaptation relevant to the current rapid development of AI systems needing to generalize across diverse, real-world data distributions.
Improving robust prediction under latent shift is crucial for deploying reliable AI, particularly in applications where data distributions vary and direct causal links are obscured.
This research contributes to refining methodologies for AI systems to adapt to new environments without strong assumptions about data completeness, enabling broader and more trustworthy AI applications.
- · AI researchers
- · Machine learning engineers
- · Domain adaptation practitioners
- · Industries relying on robust AI deployment
- · AI systems lacking robust domain adaptation capacities
- · Applications with high-stakes deployment in shifting environments
More robust and generalizable AI models can be developed for practical applications.
Increased trust and adoption of AI technologies in complex, real-world scenarios.
Accelerated integration of AI into sectors currently hesitant due to generalization limitations.
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Read at arXiv cs.LG