[D] Is a KDD publication considered prestigious for more theoretical results?

2026-02-12 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation, Data Science & Analytics · Depth: Advanced, medium

Summary

The prestige and suitability of KDD (ACM SigKDD) for publishing "theoretical" machine learning results, particularly at the intersection of ML and exact sciences, is a nuanced topic. While KDD is widely recognized as a top-tier venue for data mining, applied ML, and domain-driven advances, its perception differs from conferences like NeurIPS, ICML, or COLT, which are often associated with learning theory. KDD's new "AI for Science" track is specifically designed for work bridging ML with scientific domains like astrophysics or high-energy physics, even if the ML component itself isn't deeply theoretical. Experts suggest that KDD is highly respected in applied ML and data mining circles, and a well-cited KDD paper can be more impactful than a poorly cited NeurIPS paper, especially for career progression in industry or academia.

Key takeaway

For AI Scientists developing ML applications in highly technical scientific domains, your work may be an excellent fit for KDD, particularly its "AI for Science" track. Do not be deterred by KDD's "applied" reputation; if your primary audience includes scientists and applied ML practitioners, KDD offers a highly relevant platform for your research, potentially leading to greater impact and citations than a less-aligned theoretical venue.

Key insights

KDD is a top-tier venue for applied ML and data mining, especially for interdisciplinary scientific applications.

Principles

• Venue prestige is subcommunity-dependent.
• "Technical" is not synonymous with "theoretical."
• Citations often outweigh venue name alone.

In practice

• Consider KDD's "AI for Science" track for ML applications in exact sciences.
• Prioritize audience alignment when selecting a publication venue.

Topics

• KDD Conference
• ML Publication Venues
• Applied Machine Learning
• AI for Science
• Research Prestige

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

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