2601.07535v1.pdf

{
  "dorsal/arxiv": [
    {
      "date_modified": "2026-02-17T05:53:12.601000Z",
      "hash": "c21ecf88f0944e10b945070db2d2c23bae281eb0f4fa1a76e4c837817bf47b39",
      "id": "2621b578-8d8a-48ef-be4c-cb8259ce20a0",
      "source": {
        "execution_id": "d565f9d4-8d83-4bae-a3e1-871a5ad371a3",
        "id": "arXiv Dataset",
        "type": "Model",
        "variant": "snapshot-2026-01-17",
        "version": "0.1.0"
      },
      "user_id": 1000002
    }
  ],
  "file/base": {
    "record": {
      "extension": ".pdf",
      "hash": "c21ecf88f0944e10b945070db2d2c23bae281eb0f4fa1a76e4c837817bf47b39",
      "media_type": "application/pdf",
      "media_type_prefix": "application",
      "name": "2601.07535v1.pdf",
      "size": 230812
    },
    "source": {
      "execution_id": "acee4679-8dc6-4df2-a032-7cb5feaa4108",
      "id": "file/base",
      "type": "Model",
      "version": "1.0.0"
    }
  },
  "file/pdf": {
    "private": false,
    "record": {
      "creation_date": "2026-01-13T03:24:14Z",
      "keywords": [
        "stochastic bandits",
        "pure exploration",
        "clustering",
        "nonparametric methods",
        "distributional clustering",
        "kernel methods",
        "reproducing kernel Hilbert spaces (RKHS)",
        "maximum mean discrepancy (MMD)"
      ],
      "modified_date": "2026-01-13T03:24:14Z",
      "page_count": 14,
      "producer": "GPL Ghostscript 10.01.2",
      "subject": "Proceedings of the International Conference on Machine Learning 2025",
      "title": "Nonparametric Kernel Clustering with Bandit Feedback",
      "version": "1.4"
    },
    "source": {
      "execution_id": "50f707e5-e467-4fb5-be2a-e53ae627af8b",
      "id": "dorsal/pdf",
      "type": "Model",
      "version": "1.1.0"
    }
  }
}