stat.ME · 2026-09-23 · No. 122

Methodology, 2026-09-23.

2 new papers in stat.ME. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Automatic depth-based local center clustering via $β$-integrated local depth and adaptive grouping

    Siyi Wang, Alexandre Leblanc, Paul D. McNicholas

    stat.ME · cs.LG · stat.ML

    Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters or neighborhood size. Conversely, we propose automatic depth-based local center clustering (A-DLCC), a fully data-driven method that eliminates numerical parameter tuning. A-DLCC uses the $β$-integrated local depth to identify stable exemplars, points consistently...

    arxiv.org/abs/2609.26748 · PDF

  2. 02

    Optimal Sequential Annotations for Off-Policy Evaluation

    Woojin Chae, Ezinne Nwankwo, Haitong Qin, Angela Zhou

    stat.ME · cs.LG · stat.ML

    Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment. In recent AI applications, state and reward information is recorded as complex text or image, which recent AI advancements such as LLM-as-a-judge can label with unknown bias. Expert annotation may be available but at a higher cost. For example, safety classification via cheap but imperfect...

    arxiv.org/abs/2609.26707 · PDF

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