Abstract
Distribution shift is the defining challenge of real-world machine learning. The dominant paradigm--Unsupervised Domain Adaptation (UDA)--enforces feature invariance, aligning source and target representations via symmetric divergence minimization [Ganin et al., 2016]. We demonstrate that this approach is fundamentally flawed: when domains are unequally informative (e.g., high-quality vs degraded sensors), strict invariance necessitates information destruction, causing "negative transfer" that can be catastrophic in safety-critical applications [Wang et al., 2019]. We propose a decision-theoretic framework grounded in Le Cam's theory of statistical experiments [Le Cam, 1986], using constructive approximations to replace symmetric invariance with directional simulability. We introduce Le Cam Distortion, quantified by the Deficiency Distance $\delta(E_1, E_2)$, as a rigorous upper bound for transfer risk conditional on simulability. Our framework enables transfer without source degradation by learning a kernel that simulates the target from the source. Across five experiments (genomics, vision, reinforcement learning), Le Cam Distortion achieves: (1) near-perfect frequency estimation in HLA genomics (correlation $r=0.999$, matching classical methods), (2) zero source utility loss in CIFAR-10 image classification (81.2% accuracy preserved vs 34.7% drop for CycleGAN), and (3) safe policy transfer in RL control where invariance-based methods suffer catastrophic collapse. Le Cam Distortion provides the first principled framework for risk-controlled transfer learning in domains where negative transfer is unacceptable: medical imaging, autonomous systems, and precision medicine.
Abstract (translated)
分布迁移是现实世界机器学习面临的最大挑战。主导的方法——无监督领域适应（UDA）强制要求特征不变性，通过对称散度最小化来使源域和目标域的表示相匹配 [Ganin et al., 2016]。我们证明了这种方法本质上是有缺陷的：当领域信息量不等（如高质量传感器与退化的传感器之间），严格的不变性需要破坏某些信息，从而导致“负迁移”，这种现象在安全关键应用中可能会非常严重 [Wang et al., 2019]。我们提出了一种基于Le Cam统计实验理论的决策论框架[Le Cam, 1986]，通过构造性的近似方法将对称不变性替换为方向可模拟性。我们引入了Le Cam扭曲度量，并用匮乏距离$\delta(E_1, E_2)$进行量化，作为条件下的迁移风险严格上限。我们的框架允许在不损害源域性能的情况下实现迁移学习，通过学习一个能够从源域模拟目标域的核函数来完成这一过程。 在五项实验（基因组学、视觉和强化学习）中，Le Cam扭曲实现了以下成果： 1. 在HLA基因分型中的近乎完美的频率估计（相关系数$r=0.999$，与经典方法相当） 2. CIFAR-10图像分类任务中源域性能几乎无损失的保持（准确率从81.2%降到34.7%，而CycleGAN则下降到了56.5%） 3. 在RL控制中的安全策略迁移，在不变性方法会导致灾难性崩溃的情况下，Le Cam扭曲框架下可以实现稳定传输。 Le Cam扭曲为风险可控的学习迁移提供了第一个原则性的框架，特别适用于不允许负迁移的领域：医学成像、自主系统和精准医疗。
URL
https://arxiv.org/abs/2512.23617
