stat.ML · 2026-06-09 · No. 18

Machine Learning, 2026-06-09.

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

    Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting

    Valery Manokhin

    stat.ML · cs.LG

    Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted. We show that the simplest possible conformal interval - a last-value point forecast wrapped in a finite-sample split-conformal residual quantile, with no parameters and no training - is a far stronger baseline than its near-total absence from recent learned-forecasting and conformal-time-series comparisons would suggest....

    arxiv.org/abs/2606.09473 · PDF

  2. 02

    SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths

    Timo Heiß, Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio

    stat.ML · cs.AI · cs.LG

    Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their functional form, or visualize only restricted interaction types. We propose Surrogate-based Analysis of Interactions via Local effect Smooths (SAILS), a model-agnostic framework that analyzes pairwise interactions through interpretable generalized additive model...

    arxiv.org/abs/2606.09404 · PDF

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