mlsanityData to 14 Oct 2026

On Experiments

arXiv 2508.08288 ↗First posted 5 Aug 2025stat.ML cs.LG math.PRcited by 0

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Abstract (arXiv)

The scientific process is a means to turn the results of experiments into knowledge about the world in which we live. Much research effort has been directed toward automating this process. To do this, one needs to formulate the scientific process in a precise mathematical language. This paper outlines one such language. What is presented here is hardly new. The material is based on great thinkers from times past well as more modern contributions. The novel contributions of this paper are: A new general data processing inequality, a bias variance decomposition for canonical losses, streamlined proofs of the Blackwell-Sherman-Stein and Randomization theorems. means of calculating deficiency through linear programming.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 23 references that are in the map. First posted on arXiv 5 Aug 2025.

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References · 1

  1. 2021Convex optimizationcited by 2,515

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