论文标题
用于解决OOD算法中数据质量差异的正规化惩罚优化
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
论文作者
论文摘要
由于传统的经验风险最小化（ERM）的概括性差，因此在分布转移的情况下，分布外（OOD）概括算法受到越来越多的关注。但是，OOD的概括算法忽略了训练数据质量的巨大差异，这极大地损害了这些方法的准确性。在本文中，我们从理论上揭示了训练数据质量和算法性能之间的关系，并分析了Lipschitz正则不变风险最小化的最佳正则化方案。提出了一种基于理论结果提出的新算法，以减轻样品水平和域水平上低质量数据的影响。关于回归和分类基准的实验验证了我们方法具有统计意义的有效性。
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention. However, OoD generalization algorithms overlook the great variance in the quality of training data, which significantly compromises the accuracy of these methods. In this paper, we theoretically reveal the relationship between training data quality and algorithm performance and analyze the optimal regularization scheme for Lipschitz regularized invariant risk minimization. A novel algorithm is proposed based on the theoretical results to alleviate the influence of low-quality data at both the sample level and the domain level. The experiments on both the regression and classification benchmarks validate the effectiveness of our method with statistical significance.
