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.

The code is available at Contrastive-Sampling .

Considering the complex graph structure, are some nodes consistently well-trained and following this principle even with different graph augmentations?

We propose information gating as a way to learn parsimonious representations that identify the minimal information required for a task.

We propose contrastive coding to learn shared, dense image representations, referredtoasCoMIRs (ContrastiveMultimodal ImageRepresentations).

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP