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Tag: contrastive

  • Scalable Contrastive Causal Discovery under Unknown Soft Interventions

    Scalable Contrastive Causal Discovery under Unknown Soft Interventions arXiv:2603.03411v1 Announce Type: new Abstract: Observational causal discovery is only identifiable up to the Markov equivalence class. While interventions can reduce this ambiguity, in practice interventions are often soft with multiple unknown targets. In many realistic scenarios, only a single intervention regime is observed. We propose a…

  • PCA++: How Uniformity Induces Robustness to Background Noise in Contrastive Learning

    PCA++: How Uniformity Induces Robustness to Background Noise in Contrastive Learning arXiv:2511.12278v1 Announce Type: new Abstract: High-dimensional data often contain low-dimensional signals obscured by structured background noise, which limits the effectiveness of standard PCA. Motivated by contrastive learning, we address the problem of recovering shared signal subspaces from positive pairs, paired observations sharing the same…

  • A Mathematical Perspective On Contrastive Learning

    A Mathematical Perspective On Contrastive Learning arXiv:2505.24134v1 Announce Type: new Abstract: Multimodal contrastive learning is a methodology for linking different data modalities; the canonical example is linking image and text data. The methodology is typically framed as the identification of a set of encoders, one for each modality, that align representations within a common latent…

  • Infinite hierarchical contrastive clustering for personal digital envirotyping

    Infinite hierarchical contrastive clustering for personal digital envirotyping arXiv:2505.15022v1 Announce Type: new Abstract: Daily environments have profound influence on our health and behavior. Recent work has shown that digital envirotyping, where computer vision is applied to images of daily environments taken during ecological momentary assessment (EMA), can be used to identify meaningful relationships between environmental…

  • Generalization Analysis for Contrastive Representation Learning under Non-IID Settings

    Generalization Analysis for Contrastive Representation Learning under Non-IID Settings arXiv:2505.04937v1 Announce Type: new Abstract: Contrastive Representation Learning (CRL) has achieved impressive success in various domains in recent years. Nevertheless, the theoretical understanding of the generalization behavior of CRL is limited. Moreover, to the best of our knowledge, the current literature only analyzes generalization bounds under…

  • A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics

    A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics arXiv:2503.17538v1 Announce Type: new Abstract: Contrastive learning — a modern approach to extract useful representations from unlabeled data by training models to distinguish similar samples from dissimilar ones — has driven significant progress in foundation models. In this work, we develop a new theoretical framework…