[
    {
        "id": "osp-16376",
        "type": "article-journal",
        "title": "Efficient quadratic entropy with distance sketches",
        "author": [
            {
                "family": "Huntsman",
                "given": "Steve"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/efficient-quadratic-entropy-with-distance-sketches",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "We detail scalable methods for approximating the quadratic entropy $p^T d p$ for arbitrary distributions $p$ and common distances $d$ of negative type. We focus on the Euclidean and spherical geodesic cases, which both use random feature embeddings and projections to dramatically improve computational complexity within a simple framework. Amortization of a single large matrix multiplication and control variates further enable computation at large scale with low memory and runtime in situations where $d$ is held constant while $p$ varies. We demonstrate this with a comparison against direct pair sampling and bibliometric/scientometric examples on Open Graph Benchmark datasets, revealing papers, fields, and institutions with both particularly narrow and broad interdisciplinary reach from their citations and text features alone."
    }
]