NeurIPS 2026
NeurIPS is the largest and most prestigious academic machine learning conference. The 2026 edition's venue should be confirmed — historically NeurIPS rotates and the California (San Diego) location is unverified for 2026.
Presented by
Monday, December 7 – Saturday, December 12, 2026
6-day academic ML conference
San Diego Convention Center (verify)
San Diego Convention Center (verify) — 111 W Harbor Dr, San Diego, CA 92101
Who attends
ML researchers · academic and industry labs · ML PhD candidates
About this event
NeurIPS — what to expect
NeurIPS is the largest and most prestigious academic machine learning conference. The 2026 edition's venue should be confirmed — historically NeurIPS rotates and the California (San Diego) location is unverified for 2026.
Programming is research-paper-driven: 2,000+ accepted papers, workshops, and tutorials across foundation models, deep learning, RL, theory, and applied ML.
Included in this California directory only if the 2026 venue is in California. Verify via neurips.cc before relying on this entry.
Why it matters for Robot.com
Three reasons to track this event
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Frontier ML research lands here
Papers that define the field's direction publish at NeurIPS. Foundation models, multimodal architectures, and RL advances appear here 6-18 months before mainstream adoption.
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Industry lab recruiting
Frontier lab researchers attend NeurIPS. The strongest single event for researcher hiring.
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Robotics AI papers
Robotics AI is a meaningful sub-track at NeurIPS. Relevant for any team doing original robotics research.
How to work the show
A playbook for the team on the ground
- Verify the 2026 venue before booking — drop this entry if not in California.
- Send a senior researcher, not a generalist. The signal-to-noise requires deep ML context.
- Focus on 2-3 workshop tracks rather than the full main conference.
Robot.com fit
Where this event maps to the product family
Direct fit
Frontier ML research and researcher recruiting for any Robot.com original research program.
Adjacent fit
Long-term ML capability planning.