[D] How do you add theoretical justification to an AI/ML paper?

2026-03-21 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Advanced, short

Summary

An AI/ML researcher, primarily experienced in empirical modeling, seeks guidance on integrating theoretical justifications, such as theorems, lemmas, and proofs, into their papers. The researcher notes difficulty in framing intuitive ideas rigorously, citing an example of measuring uncertainty in attention mechanisms by analyzing different attention head outputs. They also observe that many papers reference advanced theory not covered in their postgraduate studies. The discussion highlights varying perspectives on the necessity of theoretical backing, with some suggesting that empirical observations are sufficient, while others recommend collaborating with co-authors from pure math, statistics, or physics backgrounds to develop formal sections. One comment points to the "Troubling Trends in Machine Learning Scholarship" paper (arXiv:1807.03341) as relevant to the debate.

Key takeaway

For AI scientists focused on empirical modeling, you should evaluate whether formal theoretical justification truly enhances your paper or if robust experimental validation and clear intuition are sufficient. Consider collaborating with mathematicians or statisticians for theoretical sections, or explore existing theoretical frameworks that align with your empirical observations, rather than forcing a justification where it isn't naturally supported. This approach can save time and improve clarity.

Key insights

Theoretical justification in AI/ML papers is often not strictly necessary, with empirical validation frequently sufficing.

Principles

• Empirical observation can precede theoretical conceptualization.
• Avoid adding unnecessary theoretical machinery to empirical results.

Method

To bridge empirical work with theory, consider collaborating with co-authors from pure math, statistics, or physics, or explore existing theoretical frameworks like ensemble disagreement for uncertainty quantification.

In practice

• Consult existing work on ensemble disagreement for uncertainty.
• Seek co-authors with strong mathematical backgrounds.

Topics

• Theoretical Justification
• Empirical Modeling
• Attention Mechanisms
• Uncertainty Quantification
• Academic Collaboration

Best for: AI Scientist, AI Researcher, AI Student, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.