stat.ML · 2026-06-24 · No. 33

Machine Learning, 2026-06-24.

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

01 — The papers

2 entries
  1. 01

    Model selection with proper scoring rules on data sets of time series

    Giorgio Corani, Stefano Damato, Dario Azzimonti, Lorenzo Zambon

    stat.ML · cs.LG

    We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule, we denote by the term \textit{score} the average value of the scoring rule on the test of an individual time series. For model selection, we need aggregating the values of the scores across multiple time series. Three summary statistics are commonly used for model selection: mean score, median score, and mean...

    arxiv.org/abs/2606.24715 · PDF

  2. 02

    Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web

    Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, Klaus Ackermann

    stat.ML · cs.CV · cs.LG

    Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort. In today's data-driven world, such tasks are well suited for automation. We present a new R package that uses...

    arxiv.org/abs/2606.24236 · PDF

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