从标签比例中学习：通过置信度传播引导监督学习器
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
AI总结：
提出一种基于置信度传播的迭代算法框架，通过伪标记和嵌入优化步骤解决从标签比例中学习的问题，在表格和图像数据上性能显著提升且计算开销极小。
摘要：
从标签比例中学习（LLP）是一种学习问题，在训练期间，只有称为包的实例组的聚合级别标签可用，目标是在测试数据上获得实例级别的最佳性能。由于隐私考虑，这种设定出现在广告和医学等领域。我们为这个问题提出了一种新颖的算法框架，该框架迭代地执行两个主要步骤。在每次迭代的第一个步骤（伪标记）中，我们定义了一个基于二元实例标签的吉布斯分布，该分布结合了 a) 通过相似协变量应具有相似标签的约束所传达的协变量信息，以及 b) 包级别的聚合标签。然后，我们使用置信度传播（BP）对吉布斯分布进行边缘化以获得伪标签。在第二个步骤（嵌入优化）中，我们使用伪标签为产生更好嵌入的学习器提供监督。此外，我们通过将第二步的嵌入用作下一次迭代的新协变量来再次迭代这两个步骤。在最后一次迭代中，使用伪标签训练分类器。针对各种数据集类型——表格和图像，我们的算法在LLP二元分类问题上相对于几种SOTA基线（高达15%）表现出强大的性能提升。由于置信度传播，即使在包含百万样本的情况下，对于较大的包大小，我们在标准监督学习之上只需最小的计算开销即可实现这些改进。
英文摘要：
Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework for this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a) covariate information through the constraint that instances with similar covariates should have similar labels and b) the bag level aggregated label. We then use Belief Propagation (BP) to marginalize the Gibbs distribution to obtain pseudo labels. In the second step (Embedding Refinement), we use the pseudo labels to provide supervision for a learner that yields a better embedding. Further, we iterate on the two steps again by using the second step's embeddings as new covariates for the next iteration. In the final iteration, a classifier is trained using the pseudo labels. Our algorithm displays strong gains against several SOTA baselines (up to 15%) for the LLP Binary Classification problem on various dataset types - tabular and Image. We achieve these improvements with minimal computational overhead above standard supervised learning due to Belief Propagation, for large bag sizes, even for a million samples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
