Contrastive learning produces coherent semantic feature embeddings by encouraging positive samples to cluster closely while separating negative samples. However, existing contrastive learning methods lack principled guarantees on coverage within the semantic feature space. We extend conformal prediction to this setting by introducing minimum-volume covering sets equipped with learnable generalized multi-norm constraints. We propose a method that constructs conformal sets guaranteeing user-specified coverage of positive samples while maximizing negative sample exclusion. We establish theoretically that volume minimization serves as a proxy for negative exclusion, enabling our approach to operate effectively even when negative pairs are unavailable. The positive inclusion guarantee inherits the distribution-free coverage property of conformal prediction, while negative exclusion is maximized through learned set geometry optimized on a held-out training split. Experiments on simulated and real-world image datasets demonstrate improved inclusion-exclusion trade-offs compared to standard distance-based conformal baselines.

翻译：对比学习通过促使正样本紧密聚集、负样本分离，生成连贯的语义特征嵌入。然而，现有对比学习方法缺乏对语义特征空间中覆盖范围的原则性保证。我们将共形预测扩展至这一场景，引入配备可学习广义多范数约束的最小体积覆盖集。提出一种方法，构建能够保证用户指定正样本覆盖范围的同时最大化负样本排除的共形集。我们从理论上证明体积最小化可作为负样本排除的代理，使该方法即使在没有负样本对的情况下也能有效运行。正样本包含保证继承了共形预测的无分布覆盖特性，而负样本排除则通过优化在保留训练集上的学习几何结构得到最大化。在模拟和真实图像数据集上的实验表明，与基于距离的标准共形基线相比，该方法在包含-排除权衡方面表现更优。