mlsanityData to 14 Oct 2026

On uncertainty-penalized Bayesian information criterion

arXiv 2404.16881 ↗First posted 23 Apr 2024cs.LG math.ST stat.THcited by 1

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Abstract (arXiv)

The uncertainty-penalized information criterion (UBIC) has been proposed as a new model-selection criterion for data-driven partial differential equation (PDE) discovery. In this paper, we show that using the UBIC is equivalent to employing the conventional BIC to a set of overparameterized models derived from the potential regression models of different complexity measures. The result indicates that the asymptotic property of the UBIC and BIC holds indifferently.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 4 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

2 of 4 references.

References · 2

  1. 2022Noise-aware Physics-informed Machine Learning for Robust PDE Discoverycited by 14
  2. 2023Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discoverycited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancementcited by 0